hoàn thành cơ bản các chức năng

update attribute table

.gitattributes

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-# Disabled LFS tracking to avoid pushing large files
-#*.tif filter=lfs diff=lfs merge=lfs -text
-#*.joblib filter=lfs diff=lfs merge=lfs -text
-#*.nc filter=lfs diff=lfs merge=lfs -text
-#*.ipynb filter=lfs diff=lfs merge=lfs -text
+*.tif filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.nc filter=lfs diff=lfs merge=lfs -text
+*.ipynb filter=lfs diff=lfs merge=lfs -text

.gitignore

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-# Ignore all model weights and large data
-*.joblib
-*.nc
-*.tif
-*.tiff
-*.png
-*.jpg
-*.jpeg
-*.h5
-*.pt
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-*~
-
-# Ignore model info/metadata if không cần backup
-# *.json
-
-# Ignore cache, prediction, backup folders
-dataset_cache/
-predictions/
-backup_model_train/
-backup_ketquaphanloai/
-backup_S3_download_Amazon/
-model_train/
-__pycache__/
-# Ignore large data files
-ndvi_results/
-ndvi_cache/
-prediction_cache/
-dataset_cache/
-bfg.jar
-..bfg-report/
-.dvc/
-
-# Ignore model outputs but keep info json
-model_train/*.joblib
-model_train/*.tif
-model_train/*.png
-model_train/*.h5
-model_train/*.pt
-model_train/*.pth
-model_train/*.ckpt
-model_train/*.npz
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-model_train/*.parquet
-model_train/*.feather
-model_train/*.db
-model_train/*.sqlite
-model_train/*.log
-cloud_removal_model/
-
-# VSCode settings
-.vscode/
-
-# Jupyter checkpoints
-.ipynb_checkpoints/
-reports/
+ThuanHoa/ThuanHoa_VH.tif
+ThuanHoa/ThuanHoa_VV.tif
+model_train/model.joblib
+model_train/model_new.joblib
+backup_model_train/model.joblib
+backup_model_train/model_new.joblib
+dataset_cache/sentinel2_timeseries_40scenes.nc

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CLOUD_PROCESSING.md

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-# Xử lý mây (Cloud Processing) — Hệ thống Land Classification
-
-Tài liệu chi tiết về các phương pháp xử lý mây cho dữ liệu Sentinel-2. Module độc lập `cloud_removal.py` cung cấp nhiều chiến lược có thể chọn.
-
-## Tổng quan
-
-Hệ thống cung cấp **7 phương pháp xử lý mây** khác nhau, từ cổ điển đến hiện đại (ML/DL):
-
-1. **Classic** - 3 bước cổ điển (temporal → median → spatial) - mặc định
-2. **Temporal Only** - Chỉ temporal interpolation (nhanh nhất)
-3. **Median Composite** - Ưu tiên median composite (giảm nhiễu tốt nhất)
-4. **ML KNN** - Machine Learning K-Nearest Neighbors inpainting
-5. **ML RF** - Machine Learning Random Forest inpainting
-6. **Deep Inpainting** - Deep Learning CNN inpainting (yêu cầu model)
-7. **Hybrid** - Kết hợp classical + ML (cân bằng tốc độ và chất lượng)
-
----
-
-## Cách sử dụng
-
-### API Endpoint
-
-Lấy danh sách các methods:
-```bash
-GET /api/cloud-removal/methods
-```
-
-Response:
-```json
-{
-  "success": true,
-  "methods": {
-    "classic": "3-step classical: temporal → median → spatial (default, balanced)",
-    "temporal_only": "Temporal interpolation only (fastest, needs many scenes)",
-    "median_composite": "Median composite priority (best noise reduction)",
-    "ml_knn": "ML K-Nearest Neighbors inpainting (good quality, medium speed)",
-    "ml_rf": "ML Random Forest inpainting (high quality, slower)",
-    "deep": "Deep Learning CNN inpainting (best quality, requires model)",
-    "hybrid": "Hybrid classical + ML (balanced speed & quality)"
-  },
-  "default": "classic"
-}
-```
-
-### Config trong Prediction
-
-Thêm `cloud_removal_method` vào config:
-
-```python
-config = {
-    "model_filename": "model_odc.joblib",
-    "min_lon": 105.5,
-    "max_lon": 105.6,
-    "min_lat": 10.0,
-    "max_lat": 10.1,
-    "start_date": "2024-01-01",
-    "end_date": "2024-12-31",
-    "max_scenes": 12,
-    "cloud_cover": 30,
-    "resolution": 20,
-    "use_gpu": false,
-    "cloud_removal_method": "hybrid"  # Chọn method tại đây
-}
-```
-
-### Programmatic Usage
-
-```python
-from cloud_removal import process_cloud_removal
-
-# Load Sentinel-2 data with SCL band
-s2_data = load(...) 
-
-# Process clouds with selected method
-cleaned_data, metadata = process_cloud_removal(
-    s2_data=s2_data,
-    method="hybrid",  # or "classic", "ml_knn", etc.
-    verbose=True
-)
-
-print(f"Cloud coverage: {metadata['cloud_coverage_percent']:.1f}%")
-print(f"Steps applied: {metadata['steps_applied']}")
-```
-
----
-
-## Chi tiết các phương pháp
-
-### 1. Classic (Mặc định)
-
-**Mô tả:** 3 bước cổ điển kết hợp temporal, median, và spatial interpolation.
-
-**Quy trình:**
-1. Temporal interpolation (ffill + bfill)
-2. Median compositing (nếu >= 3 scenes)
-3. Spatial interpolation (nearest neighbor)
-4. Fallback fillna(0)
-
-**Ưu điểm:**
-- Cân bằng tốc độ và chất lượng
-- Đã được test kỹ, ổn định
-- Phù hợp hầu hết trường hợp
-
-**Nhược điểm:**
-- Không tối ưu cho các gaps lớn
-- Có thể tạo artifacts ở biên
-
-**Khi nào dùng:** Default choice, phù hợp cho production
-
----
-
-### 2. Temporal Only
-
-**Mô tả:** Chỉ sử dụng temporal interpolation (ffill + bfill).
-
-**Ưu điểm:**
-- Nhanh nhất
-- Giữ xu hướng thời gian tốt
-- Ít tạo artifacts
-
-**Nhược điểm:**
-- Yêu cầu nhiều time steps
-- Không xử lý được gaps liên tục
-- Chất lượng kém nếu ít scenes
-
-**Khi nào dùng:** Khi có nhiều scenes (>10) và cần tốc độ
-
----
-
-### 3. Median Composite
-
-**Mô tả:** Ưu tiên median composite, sau đó spatial interpolation.
-
-**Ưu điểm:**
-- Giảm nhiễu tốt nhất
-- Chống outliers hiệu quả
-- Tạo composite trơn
-
-**Nhược điểm:**
-- Mất thông tin temporal
-- Yêu cầu >= 3 scenes
-- Chậm hơn temporal only
-
-**Khi nào dùng:** Khi cần giảm nhiễu, không quan tâm temporal dynamics
-
----
-
-### 4. ML KNN Inpainting
-
-**Mô tả:** Sử dụng K-Nearest Neighbors để học từ pixels hợp lệ và dự đoán pixels bị mây.
-
-**Quy trình:**
-1. Xác định valid pixels (không có mây)
-2. Train KNN model với spatial coordinates + spectral values
-3. Predict invalid pixels
-4. Fill predictions vào dataset
-
-**Ưu điểm:**
-- Chất lượng cao hơn classical
-- Học spatial patterns
-- Không cần pretrained model
-
-**Nhược điểm:**
-- Chậm hơn classical
-- Yêu cầu đủ valid pixels (>10)
-- Tốn RAM nếu ảnh lớn
-
-**Hyperparameters:**
-- n_neighbors: 5
-- weights: 'distance'
-
-**Khi nào dùng:** Khi cần chất lượng cao và có đủ valid pixels
-
----
-
-### 5. ML Random Forest Inpainting
-
-**Mô tả:** Sử dụng Random Forest để inpainting, tương tự KNN nhưng phức tạp hơn.
-
-**Ưu điểm:**
-- Chất lượng cao nhất trong ML methods
-- Xử lý non-linear patterns tốt
-- Robust với outliers
-
-**Nhược điểm:**
-- Chậm nhất trong ML methods
-- Tốn nhiều RAM
-- Có thể overfit với ít data
-
-**Hyperparameters:**
-- n_estimators: 10
-- max_depth: 10
-- n_jobs: -1 (parallel)
-
-**Khi nào dùng:** Khi cần chất lượng tối đa và không quan tâm tốc độ
-
----
-
-### 6. Deep Inpainting (CNN)
-
-**Mô tả:** Sử dụng CNN autoencoder để reconstruct pixels bị mây.
-
-**Trạng thái:** **Đang phát triển** - yêu cầu pretrained model
-
-**Quy trình (planned):**
-1. Stack bands thành multi-channel image
-2. Tạo binary mask (1=cloud, 0=valid)
-3. Run through CNN autoencoder
-4. Blend predictions với valid pixels
-
-**Ưu điểm (khi có model):**
-- Chất lượng tốt nhất
-- Xử lý large gaps hiệu quả
-- Học global context
-
-**Nhược điểm:**
-- Yêu cầu pretrained model
-- Chậm nhất (GPU recommended)
-- Phức tạp để deploy
-
-**Khi nào dùng:** Khi có GPU và pretrained model, cần chất lượng tối đa
-
----
-
-### 7. Hybrid (Khuyến nghị)
-
-**Mô tả:** Kết hợp classical + ML để cân bằng tốc độ và chất lượng.
-
-**Quy trình:**
-1. Temporal interpolation (nhanh)
-2. Check remaining NaN percentage
-3. Nếu > 5%: Apply ML KNN inpainting
-4. Nếu <= 5%: Apply spatial interpolation
-5. Fallback fillna(0)
-
-**Ưu điểm:**
-- Cân bằng tốc độ và chất lượng
-- Adaptive - chỉ dùng ML khi cần
-- Hiệu quả với mọi cloud coverage
-
-**Nhược điểm:**
-- Phức tạp hơn classic
-- Khó debug
-
-**Khi nào dùng:** **Khuyến nghị cho production** - tự động chọn strategy phù hợp
-
----
-
-## So sánh Performance
-
-| Method | Tốc độ | Chất lượng | RAM | Yêu cầu |
-|--------|--------|------------|-----|---------|
-| classic | ⭐⭐⭐⭐ | ⭐⭐⭐ | Thấp | Không |
-| temporal_only | ⭐⭐⭐⭐⭐ | ⭐⭐ | Thấp | Nhiều scenes |
-| median_composite | ⭐⭐⭐ | ⭐⭐⭐⭐ | Thấp | >= 3 scenes |
-| ml_knn | ⭐⭐ | ⭐⭐⭐⭐ | Trung bình | Đủ valid pixels |
-| ml_rf | ⭐ | ⭐⭐⭐⭐⭐ | Cao | Đủ valid pixels |
-| deep | ⭐ | ⭐⭐⭐⭐⭐ | Rất cao | Pretrained model + GPU |
-| hybrid | ⭐⭐⭐ | ⭐⭐⭐⭐ | Trung bình | Không |
-
----
-
-## Phát hiện mây (SCL)
-
-Tất cả methods đều sử dụng SCL (Scene Classification Layer):
-
-```python
-# SCL values:
-# 0: No data, 1: Saturated/Defective, 2: Dark Area Pixels
-# 3: Cloud shadows, 4: Vegetation, 5: Not vegetated, 6: Water
-# 7: Unclassified, 8: Cloud medium probability, 9: Cloud high probability
-# 10: Thin cirrus, 11: Snow/Ice
-
-cloud_mask = (scl == 3) | (scl == 8) | (scl == 9) | (scl == 10) | (scl == 11)
-invalid_mask = (scl == 0) | (scl == 1)
-full_mask = cloud_mask | invalid_mask
-```
-
----
-
-## Testing & Comparison
-
-So sánh nhiều methods trên cùng dữ liệu:
-
-```python
-from cloud_removal import compare_methods
-
-results = compare_methods(
-    s2_data=s2_data,
-    methods=["classic", "temporal_only", "ml_knn", "hybrid"]
-)
-
-for method, result in results.items():
-    print(f"{method}: {result['remaining_nan_percent']:.2f}% NaN remaining")
-```
-
----
-
-## Khuyến nghị sử dụng
-
-### Production (General)
-```
-cloud_removal_method: "hybrid"
-```
-- Cân bằng tốc độ và chất lượng
-- Adaptive theo cloud coverage
-
-### High Quality (Research)
-```
-cloud_removal_method: "ml_rf"
-```
-- Chất lượng tối đa
-- Chấp nhận tốc độ chậm
-
-### Fast Processing (Monitoring)
-```
-cloud_removal_method: "temporal_only"
-```
-- Cần nhiều scenes (>10)
-- Ưu tiên tốc độ
-
-### Low Cloud Coverage (<10%)
-```
-cloud_removal_method: "classic"
-```
-- Đơn giản, hiệu quả
-- Ổn định, đã test kỹ
-
----
-
-## Vị trí code
-
-- **Module:** `cloud_removal.py` - Standalone cloud removal module
-- **API Integration:** `api_server.py` - API endpoints và config
-- **Documentation:** `CLOUD_PROCESSING.md` - Tài liệu này
-
----
-
-## Phát triển tiếp
-
-- [ ] Implement CNN autoencoder cho deep inpainting
-- [ ] Add quality scoring system
-- [ ] Optimize ML methods với Dask
-- [ ] Add weighted temporal interpolation
-- [ ] Support custom ML models
-

CLOUD_REMOVAL_UPLOAD_GUIDE.md

@@ -1,200 +0,0 @@
-# Cloud Removal Model Upload Feature
-
-## Overview
-Added functionality to upload and use custom deep learning cloud removal models (.pth files) during prediction.
-
-## Features Implemented
-
-### 1. API Endpoints
-
-#### Upload Cloud Removal Model
-```
-POST /api/cloud-removal/upload
-```
-- Upload `.pth` cloud removal model files
-- Validates file extension (.pth only)
-- Security checks for filename
-- Returns file info (name, size)
-
-**Example:**
-```bash
-curl -X POST -F "file=@cloud_removal_unet_best.pth" \
-  http://localhost:8000/api/cloud-removal/upload
-```
-
-#### List Cloud Removal Models
-```
-GET /api/cloud-removal/models
-```
-Already existing - lists all `.pth` models in `model_train/` directory
-
-#### Delete Cloud Removal Model
-```
-DELETE /api/cloud-removal/models/{filename}
-```
-Already existing - deletes a specific cloud removal model
-
-### 2. Prediction Configuration Updates
-
-#### PredictionConfig
-Added new optional field:
-```python
-cloud_removal_model: Optional[str] = None  # .pth filename
-```
-
-#### PredictionWithNDVIConfig  
-Added new optional field:
-```python
-cloud_removal_model: Optional[str] = None  # .pth filename
-```
-
-### 3. Prediction Function Integration
-
-The `run_prediction()` function now:
-1. Accepts `cloud_removal_model` parameter
-2. Passes model path to `process_cloud_removal()`
-3. Logs which model is being used
-
-**Code:**
-```python
-cloud_removal_method = config.cloud_removal_method
-cloud_removal_model = config.cloud_removal_model
-
-s2_data, cloud_metadata = process_cloud_removal(
-    s2_data=s2_data,
-    method=cloud_removal_method,
-    model_path=f"model_train/{cloud_removal_model}" if cloud_removal_model else None,
-    verbose=True
-)
-```
-
-### 4. Web Interface Updates
-
-#### Upload Button
-- Added file input in "Deep Learning" cloud removal section
-- Upload button appears when "Deep Learning" method is selected
-- Real-time upload status feedback
-- Auto-refreshes model list after successful upload
-
-#### Model Selection
-- Dropdown shows all available `.pth` models
-- Auto-selects newly uploaded model
-- Shows model metadata (epoch, loss)
-
-## Usage Guide
-
-### Step 1: Train or Obtain a Cloud Removal Model
-Train using the cloud training interface or obtain a pre-trained `.pth` model.
-
-### Step 2: Upload Model
-1. Go to Prediction Interface
-2. Scroll to "Cloud Removal Method" section
-3. Select "Deep Learning (U-Net)" from dropdown
-4. Model upload section appears
-5. Click "📤 Upload Cloud Removal Model (.pth)"
-6. Select your `.pth` file
-7. Wait for upload confirmation
-
-### Step 3: Use Model in Prediction
-1. The uploaded model is automatically selected
-2. Configure other prediction parameters (bbox, dates, etc.)
-3. Click "🚀 Start Prediction (với NDVI)"
-4. The system will use your custom model for cloud removal
-
-## File Structure
-```
-model_train/
-├── cloud_removal_unet_best.pth         # User uploaded
-├── cloud_removal_unet_epoch_10.pth     # User uploaded
-├── model_mobilenet-lraspp_*.joblib     # Land classification models
-└── ...
-```
-
-## API Request Example
-
-### Using Uploaded Model
-```json
-{
-  "model_filename": "model_mobilenet-lraspp_20260105_225459.joblib",
-  "min_lon": 105.80,
-  "min_lat": 10.00,
-  "max_lon": 105.82,
-  "max_lat": 10.02,
-  "start_date": "2024-01-15",
-  "end_date": "2024-01-17",
-  "max_scenes": 3,
-  "cloud_cover": 30,
-  "resolution": 20,
-  "use_gpu": true,
-  "export_ndvi": true,
-  "export_classification": true,
-  "cloud_removal_method": "deep",
-  "cloud_removal_model": "cloud_removal_unet_best.pth"
-}
-```
-
-### Without Custom Model (Classical Methods)
-```json
-{
-  ...
-  "cloud_removal_method": "hybrid",
-  "cloud_removal_model": null
-}
-```
-
-## Security Features
-- Filename validation (no path traversal)
-- File extension validation (.pth only)
-- File existence checks
-- Duplicate filename detection
-
-## Error Handling
-- Invalid file type → 400 Bad Request
-- Duplicate filename → 400 Bad Request  
-- Upload failure → 500 Internal Server Error
-- Missing model when "deep" selected → Falls back to "hybrid" method
-
-## Notes
-- Uploaded models are stored in `model_train/` directory
-- Models must be PyTorch `.pth` files
-- Compatible with `cloud_removal.py` module
-- Works with both `/api/prediction/start` and `/api/predict/with-ndvi` endpoints
-
-## Testing
-
-### Test Upload
-```bash
-# Upload a model
-curl -X POST -F "file=@my_cloud_model.pth" \
-  http://localhost:8000/api/cloud-removal/upload
-
-# List models
-curl http://localhost:8000/api/cloud-removal/models
-
-# Delete model
-curl -X DELETE \
-  http://localhost:8000/api/cloud-removal/models/my_cloud_model.pth
-```
-
-### Test Prediction
-```bash
-curl -X POST http://localhost:8000/api/predict/with-ndvi \
-  -H "Content-Type: application/json" \
-  -d '{
-    "model_filename": "model_mobilenet-lraspp_20260105_225459.joblib",
-    "min_lon": 105.80, "min_lat": 10.00,
-    "max_lon": 105.82, "max_lat": 10.02,
-    "start_date": "2024-01-15", "end_date": "2024-01-17",
-    "max_scenes": 2, "cloud_cover": 30, "resolution": 20,
-    "use_gpu": false, "export_ndvi": true,
-    "cloud_removal_method": "deep",
-    "cloud_removal_model": "cloud_removal_unet_best.pth"
-  }'
-```
-
-## Future Enhancements
-- Model metadata display (architecture, training date)
-- Model validation on upload
-- Multiple model format support (.pt, .onnx)
-- Model performance metrics
-- Batch upload support

CLOUD_TRAINING_GUIDE.md

@@ -1,227 +0,0 @@
-# Cloud Removal Training với SEN12MS-CR Dataset
-
-Hướng dẫn train Deep Learning model để khử mây từ ảnh Sentinel-2 sử dụng dataset SEN12MS-CR.
-
-## 📂 Cấu trúc dữ liệu
-
-```
-winter_dataset/
-├── ROIs2017_winter_s1/          # Sentinel-1 SAR data (VV, VH)
-│   ├── s1_8/
-│   ├── s1_9/
-│   └── ...
-├── ROIs2017_winter_s2/          # Sentinel-2 CLEAN (ground truth)
-│   ├── s2_8/
-│   ├── s2_9/
-│   └── ...
-├── ROIs2017_winter_s2_cloudy/   # Sentinel-2 CLOUDY (input)
-│   ├── s2_cloudy_8/
-│   ├── s2_cloudy_9/
-│   └── ...
-└── sen12ms_cr_dataLoader.py     # Data loader
-```
-
-## 🚀 Quick Start
-
-### 1. Training Model
-
-```bash
-# Activate environment
-conda activate env_01
-
-# Train cloud removal model
-python train_cloud_removal.py
-```
-
-**Hyperparameters mặc định:**
-- Use S1: `True` (sử dụng radar data)
-- Batch size: `8`
-- Epochs: `50`
-- Learning rate: `1e-4`
-- Model: U-Net
-- Loss: MAE (L1 Loss)
-
-### 2. Test Training (Quick)
-
-```bash
-# Test với 5 epochs
-python test_cloud_training.py
-```
-
-### 3. Sử dụng Model đã train
-
-```python
-from cloud_removal import process_cloud_removal
-
-# Load Sentinel-2 data
-s2_data = load(...)  # Your S2 data with SCL band
-
-# Apply deep learning cloud removal
-cleaned_data, metadata = process_cloud_removal(
-    s2_data=s2_data,
-    method="deep",  # Use deep learning method
-    verbose=True
-)
-```
-
-## 🎯 Model Architecture
-
-**U-Net** với cấu trúc:
-- **Input:** S2 cloudy (4 bands: B02, B03, B04, B08) + S1 (2 bands: VV, VH) = 6 channels
-- **Output:** S2 clean (4 bands) = 4 channels
-- **Features:** [64, 128, 256, 512]
-- **Skip connections:** Encoder → Decoder
-- **Activation:** ReLU + BatchNorm
-
-## 📊 Dataset Info
-
-**SEN12MS-CR** (Sentinel-12 Multi-Seasonal Cloud Removal):
-- **Scenes:** ~2000+ patches
-- **Size:** 256x256 pixels
-- **Bands:**
-  - S1: VV, VH (2 channels)
-  - S2: 13 bands (chọn B02, B03, B04, B08 cho training)
-- **Seasons:** Spring, Summer, Fall, Winter
-- **Source:** [https://github.com/PatrickTUM/SEN12MS-CR](https://github.com/PatrickTUM/SEN12MS-CR)
-
-## 🔧 Customization
-
-### Thay đổi hyperparameters
-
-```python
-from train_cloud_removal import train_cloud_removal_model
-
-model, train_losses, val_losses = train_cloud_removal_model(
-    data_dir="winter_dataset",
-    use_s1=True,           # Có dùng S1 không
-    batch_size=16,         # Tăng nếu có GPU mạnh
-    num_epochs=100,        # Số epochs
-    learning_rate=5e-5,    # Learning rate
-    device="cuda",         # "cuda" hoặc "cpu"
-    save_dir="model_train" # Thư mục lưu model
-)
-```
-
-### Chỉ dùng S2 (không dùng S1)
-
-```python
-model, train_losses, val_losses = train_cloud_removal_model(
-    use_s1=False,  # Không dùng radar data
-    # ... other params
-)
-```
-
-### Thay đổi S2 bands
-
-Sửa trong `train_cloud_removal.py`:
-
-```python
-# Thay vì RGB + NIR
-s2_bands = [S2Bands.B02, S2Bands.B03, S2Bands.B04, S2Bands.B08]
-
-# Có thể dùng tất cả bands
-s2_bands = S2Bands.ALL
-```
-
-## 📈 Monitoring Training
-
-Model tự động lưu:
-- **Best model:** `model_train/cloud_removal_unet_best.pth`
-- **Training curves:** `model_train/training_curves.png`
-- **Visualizations:** `model_train/cloud_removal_epoch_*.png` (mỗi 10 epochs)
-
-## 🌐 Tích hợp vào API
-
-Model đã được tích hợp vào `cloud_removal.py`:
-
-```python
-# API endpoint
-GET /api/cloud-removal/methods
-
-# Response
-{
-  "methods": {
-    "deep": "Deep Learning U-Net inpainting (best quality, requires model)"
-  }
-}
-```
-
-Sử dụng trong prediction:
-
-```json
-{
-  "model_filename": "model_odc.joblib",
-  "cloud_removal_method": "deep",
-  "..."
-}
-```
-
-## 📝 Notes
-
-### GPU Requirements
-- **Recommended:** NVIDIA GPU với >= 6GB VRAM
-- **Minimum:** CPU (chậm hơn ~10x)
-
-### Training Time
-- **GPU (RTX 3060):** ~2-3 hours cho 50 epochs
-- **CPU:** ~20-30 hours cho 50 epochs
-
-### Data Download
-Nếu chưa có dữ liệu, download từ:
-```bash
-# Download SEN12MS-CR dataset
-wget https://mediatum.ub.tum.de/download/1554803/1554803.zip
-unzip 1554803.zip -d winter_dataset/
-```
-
-## 🐛 Troubleshooting
-
-### 1. CUDA out of memory
-```python
-# Giảm batch size
-batch_size=4  # hoặc 2
-```
-
-### 2. Import error
-```bash
-# Kiểm tra dependencies
-pip install torch torchvision tqdm matplotlib
-```
-
-### 3. Model không load được
-```python
-# Kiểm tra path
-model_path = "model_train/cloud_removal_unet_best.pth"
-assert Path(model_path).exists()
-```
-
-## 📚 References
-
-- **Paper:** SEN12MS-CR: A Dataset for Cloud Removal in Sentinel-2 Imagery
-- **GitHub:** https://github.com/PatrickTUM/SEN12MS-CR
-- **U-Net:** Ronneberger et al., "U-Net: Convolutional Networks for Biomedical Image Segmentation"
-
-## ✅ Checklist
-
-- [x] Data loader cho SEN12MS-CR
-- [x] U-Net architecture
-- [x] Training script
-- [x] Visualization
-- [x] Model saving/loading
-- [x] Tích hợp vào cloud_removal.py
-- [x] API integration
-- [x] Test script
-- [x] Documentation
-
-## 🎓 Next Steps
-
-1. **Train model:** `python train_cloud_removal.py`
-2. **Evaluate:** Xem visualizations trong `model_train/`
-3. **Test inference:** Dùng `test_cloud_removal.py`
-4. **Deploy:** Model tự động được dùng khi chọn `cloud_removal_method="deep"`
-
----
-
-**Tác giả:** AI Assistant  
-**Ngày tạo:** 2026-01-21  
-**Version:** 1.0

GEMINI_PROJECT_CONTEXT.md

@@ -1,638 +0,0 @@
-# GEMINI PROJECT CONTEXT - Land Classification & Remote Sensing System
-
-**Last Updated**: March 26, 2026  
-**Project Location**: `/home/x79/remote-sensing`  
-**Purpose**: Complete land classification and environmental monitoring system using satellite remote sensing for Vietnam
-
----
-
-## 📋 PROJECT OVERVIEW
-
-### High-Level Purpose & Problem Domain
-- **Core Task**: Classify land use/land cover (8 land classes) in Vietnam using multispectral Sentinel-2 and radar Sentinel-1 data from Microsoft Planetary Computer
-- **Geographic Focus**: Vietnam provinces/regions with bounding-box (bbox) based Area-of-Interest (AOI) selection
-- **Key Capabilities**:
-  - Dynamic training with user-selected regions and time periods
-  - Pixel-wise inference (prediction) on new regions
-  - Cloud removal using 7 different strategies
-  - NDVI time-series forecasting and change detection workflows
-  - Auto-generated HTML reports with visualizations
-  - Batch processing of multiple regions
-  - Model lifecycle management (save, load, validate, delete)
-
-### Data Pipeline
-```
-Sentinel-2 (optical) + Sentinel-1 (SAR) 
-    ↓
-[Feature Extraction: 4 modes - simple (3) / temporal (39) / extended (15) / odc (8)]
-    ↓
-[Model Training: XGBoost, RF, SVM, CNN, Swin-UNet, MobileNet-LRASPP]
-    ↓
-[Prediction: Pixel-wise classification]
-    ↓
-[Output: GeoTIFF + PNG preview + HTML report + JSON metadata]
-```
-
----
-
-## 🏗️ SYSTEM ARCHITECTURE
-
-### Core Technology Stack
-- **Backend**: FastAPI (~4200 lines in `api_server.py`)
-- **ML Training**: scikit-learn (XGBoost, RF, SVM, DT) + PyTorch (CNN, Swin-UNet, MobileNet)
-- **Geospatial**: rasterio, rioxarray, geopandas, xarray, odc.stac
-- **Data Access**: Microsoft Planetary Computer STAC API (Sentinel-2 L2A, Sentinel-1 RTC)
-- **Frontend**: HTML + Leaflet.js (map drawing) + Fetch API + Chart.js
-- **GPU Support**: PyTorch with CUDA 12.x (optional fallback to CPU)
-
-### Folder Structure
-```
-remote-sensing/
-├── Core Backend
-│   ├── api_server.py                 # FastAPI app (~4200 LOC, 70+ endpoints)
-│   ├── train_module.py               # Training pipeline engine
-│   ├── feature_extractor.py          # Unified feature extraction (4 modes)
-│   ├── model_manager.py              # Model lifecycle management
-│   ├── cloud_removal.py              # 7 cloud removal strategies
-│   ├── report_generator.py           # Auto HTML/PNG report generation
-│   ├── generate_previews.py          # GeoTIFF → PNG conversion
-│   │
-├── Utilities & Lookup
-│   ├── vietnam_provinces.py          # Province bboxes & metadata
-│   ├── vietnam_provinces_merged.py   # 32-province variant
-│   ├── utils.py                      # Geospatial helper functions
-│   ├── create_odc_metadata.py        # Metadata generator utility
-│   │
-├── Frontend Pages (HTML)
-│   ├── index.html                    # Main dashboard hub
-│   ├── training_interface.html       # Training UI
-│   ├── prediction_interface.html     # Prediction UI
-│   ├── batch_interface.html          # Batch processing UI
-│   ├── ndvi_interface.html           # NDVI time-series UI
-│   ├── dashboard.html                # Analytics dashboard
-│   ├── reports_interface.html        # Reports management
-│   ├── change_detection_interface.html # Change detection UI
-│   ├── cloud_training_interface.html # Cloud removal training UI
-│   │
-├── Tests & Notebooks
-│   ├── test_*.py                     # Unit & integration tests
-│   ├── 01.train_ODC*.ipynb           # Training notebooks
-│   ├── 02.predict_ODC.ipynb          # Prediction notebooks
-│   ├── cloud_removal_train.ipynb     # Cloud removal training
-│   │
-├── Model Storage & Caches
-│   ├── model_train/                  # Trained models (*.joblib, *.pth)
-│   │   ├── model_odc.joblib          # Legacy GridSearchCV model
-│   │   ├── model_*_info.json         # Metadata sidecar files
-│   ├── cloud_removal_model/          # Cloud removal U-Net models (.pth)
-│   ├── predictions/                  # Prediction output (GeoTIFF + PNG)
-│   ├── reports/                      # Generated HTML reports
-│   ├── dataset_cache/                # Cached Sentinel data (optional)
-│   │
-├── Config & Documentation
-│   ├── requirement.txt               # Python dependencies
-│   ├── requirements_api.txt          # API-specific deps
-│   ├── IMPLEMENTATION_SUMMARY.md     # Model manager summary
-│   ├── MODEL_MANAGER_GUIDE.md        # Full model management guide
-│   ├── NDVI_FORECAST_METHODOLOGY.md  # NDVI algorithm docs
-│   ├── CLOUD_TRAINING_GUIDE.md       # Cloud removal training guide
-│   └── [Other guides & docs]
-```
-
----
-
-## 🔧 MAIN MODULES & RESPONSIBILITIES
-
-| **Module** | **File(s)** | **Key Responsibility** |
-|---|---|---|
-| **API Server** | `api_server.py` | FastAPI app with 70+ endpoints; routes all training, prediction, batch, cloud removal, dashboard, reports, model management tasks |
-| **Training Engine** | `train_module.py` | Complete training pipeline: fetch data → feature extraction → train/test split → model training → evaluation → save with metadata |
-| **Feature Extraction** | `feature_extractor.py` | Standardized feature extraction with 4 modes: simple, temporal, extended, odc; used by both training and prediction |
-| **Model Manager** | `model_manager.py` | Lifecycle management: list, load, save, validate, delete models; handles metadata JSON; auto-detects CNN/PyTorch models |
-| **Cloud Removal** | `cloud_removal.py` | 7 cloud removal strategies: classic (3-step), temporal_only, median_composite, none, speckle filter, ML inpainting, deep learning U-Net |
-| **Report Generator** | `report_generator.py` | Auto-generates HTML/PNG reports with confusion matrices, class distributions, accuracy trends |
-| **Preview Generator** | `generate_previews.py` | Converts GeoTIFF outputs to PNG previews (NDVI or classification rasters) |
-| **Province Lookup** | `vietnam_provinces*.py` | Lookup tables for 32+ Vietnamese provinces with bboxes and region grouping |
-| **Utilities** | `utils.py` | Geospatial helper functions (load GeoDataFrames, etc.) |
-
----
-
-## 📊 END-TO-END WORKFLOWS
-
-### 1. TRAINING WORKFLOW
-```
-User Input → Training Configuration
-    ↓
-API Endpoint: POST /api/training/start
-    ↓
-train_module.py: train_model()
-    1. Fetch Sentinel-2 & Sentinel-1 from Planetary Computer STAC
-    2. Apply cloud mask (SCL band: clouds, shadows, cirrus masked)
-    3. Extract features via FeatureExtractor (mode: simple/temporal/extended/odc)
-    4. Train/test split (default 0.2)
-    5. Train selected model type (XGBoost, RF, CNN, Swin-UNet, MobileNet)
-    6. Evaluate: accuracy, precision, recall, F1, confusion matrix
-    ↓
-model_manager.py: Save model + JSON metadata
-    ↓
-report_generator.py: Auto-generate HTML training report
-    ↓
-Return: {model_filename, accuracy_metrics, training_time}
-```
-
-**Key Metadata Saved**:
-```json
-{
-  "timestamp": "2026-03-26T14:30:00",
-  "model_type": "xgboost",
-  "feature_mode": "temporal",
-  "n_features": 39,
-  "n_classes": 8,
-  "features": ["NDVI_t1", "NDVI_t2", ..., "NDWI_t1", ...],
-  "test_accuracy": 0.85,
-  "train_accuracy": 0.92,
-  "bbox": [105.6, 9.3, 106.2, 9.8],
-  "time_range": "2023-03-01/2023-05-31",
-  "resolution": 20,
-  "data_source": "Microsoft Planetary Computer STAC"
-}
-```
-
-### 2. PREDICTION WORKFLOW
-```
-User Input → Prediction Configuration (model_filename, bbox, time_range, cloud_strategy)
-    ↓
-API Endpoint: POST /api/predict or POST /api/predict/with-ndvi
-    ↓
-run_prediction() function:
-    1. Load model via model_manager.py (retrieves metadata, feature requirements)
-    2. Fetch Sentinel-2 & Sentinel-1 for new region
-    3. Apply chosen cloud_removal_method (classic/temporal_only/median_composite/none/deep_learning)
-    4. Extract features matching model's metadata requirements
-    5. Auto-adjust if feature count mismatch (pad/trim)
-    6. Predict land class for each pixel
-    7. (Optional) Calculate NDVI: (NIR - Red) / (NIR + Red)
-    8. Save outputs: GeoTIFF + PNG preview
-    ↓
-generate_previews.py: Create PNG from GeoTIFF
-    ↓
-report_generator.py: Generate prediction report
-    ↓
-Return: {prediction_file, ndvi_file, class_distribution, statistics}
-```
-
-### 3. BATCH PROCESSING WORKFLOW
-```
-User uploads CSV with multiple regions:
-  (name, min_lon, min_lat, max_lon, max_lat, start_date, end_date, max_scenes, cloud_cover, resolution)
-    ↓
-API Endpoint: POST /api/batch/start
-    ↓
-Enqueue all regions; process sequentially
-    ↓
-For each region: Run same prediction workflow
-    ↓
-Track status per region: Queued → Running → Completed/Failed
-    ↓
-UI shows progress bar, auto-retry on failure (max 3 retries)
-    ↓
-Return: Bulk results with per-region status & output files
-```
-
-### 4. CLOUD REMOVAL WORKFLOW
-```
-User selects cloud_removal_method in prediction config:
-    ↓
-cloud_removal.py: process_cloud_removal()
-    ↓
-Strategy Selection:
-  • 'classic': temporal interpolation → median composite → spatial interpolation (3-step)
-  • 'temporal_only': ffill + bfill across time dimension (fast, good for many scenes)
-  • 'median_composite': Prioritize median across scenes (best for noise reduction)
-  • 'none': Keep original, just fill NaN with 0
-  • 'deep': Use trained U-Net model (S2 cloudy + S1 → clean S2)
-  • 'ml_inpainting': KNN or Random Forest based inpainting
-  • 'speckle_filter': Reduce radar noise
-    ↓
-Return cleaned Sentinel-2 data for subsequent feature extraction
-```
-
-### 5. NDVI TIME-SERIES WORKFLOW
-```
-User requests NDVI calculation (bbox + time_range + aggregation)
-    ↓
-API Endpoint: POST /api/ndvi/timeseries or /api/ndvi/predict-timeseries
-    ↓
-Load Sentinel-2 (B04 Red, B08 NIR)
-    ↓
-Calculate NDVI = (NIR - Red) / (NIR + Red + 0.00001)
-    ↓
-Resample to monthly or user-defined aggregation
-    ↓
-Export as GeoTIFF + PNG visualization
-    ↓
-Show time-series graph & statistics (mean, min, max, std, trend)
-```
-
-### 6. CHANGE DETECTION WORKFLOW
-```
-User selects: model + current_period + prediction_period
-    ↓
-API Endpoint: POST /api/change-detection/compare-periods
-    ↓
-Run prediction for both time periods
-    ↓
-Compute difference map (current - prediction)
-    ↓
-Classify changes: increased vegetation, decreased vegetation, stable
-    ↓
-Generate change map GeoTIFF + report with statistics
-```
-
----
-
-## 🌐 API ENDPOINTS SUMMARY (70+ endpoints)
-
-### Model Management
-- `GET /api/models/list` - List all trained models with metadata
-- `GET /api/models/{filename}/info` - Get model details
-- `GET /api/models/{filename}/validate` - Validate model integrity
-- `DELETE /api/models/{filename}` - Delete model file
-
-### Training APIs
-- `POST /api/training/start` - Start land classification training
-- `GET /api/training/status` - Get training progress
-- `POST /api/training/stop` - Cancel ongoing training
-- `POST /api/cloud-removal/train` - Train cloud removal U-Net
-
-### Prediction APIs
-- `POST /api/predict` - Standard prediction (classification only)
-- `POST /api/predict/with-ndvi` - Prediction with NDVI export
-- `POST /api/change-detection/compare-periods` - Change detection
-- `GET /api/prediction/status` - Check prediction progress
-- `GET /api/predictions/list` - List prediction outputs
-- `GET /api/predictions/download/{filename}` - Download prediction file
-- `GET /api/predictions/preview/{filename}` - View PNG preview
-
-### Batch Processing
-- `POST /api/batch/start` - Enqueue multiple predictions from CSV
-- `GET /api/batch/status` - Check batch queue
-- `GET /api/batch/results/{batch_id}` - Retrieve batch results
-- `POST /api/batch/cancel/{batch_id}` - Cancel batch job
-
-### Cloud Removal
-- `GET /api/cloud-removal/methods` - List available strategies
-- `GET /api/cloud-removal/models` - List trained .pth models
-- `POST /api/cloud-removal/upload` - Upload .pth cloud removal model
-- `DELETE /api/cloud-removal/models/{filename}` - Delete cloud removal model
-
-### Dashboard & Reports
-- `GET /api/dashboard/statistics` - Overall system stats
-- `GET /api/dashboard/accuracy-trends` - Accuracy over time
-- `GET /api/dashboard/class-distribution/{model_filename}` - Class distribution
-- `GET /api/reports/list` - List generated reports
-- `GET /api/reports/view/{filename}` - View HTML report
-- `GET /api/reports/download/{filename}` - Download report
-- `DELETE /api/reports/delete/{filename}` - Delete report
-
-### Provinces & Utilities
-- `GET /api/provinces/list` - List all Vietnamese provinces
-- `GET /api/provinces/by-region` - Group provinces by region
-- `GET /api/provinces/{province_name}/bbox` - Get province bbox
-- `GET /api/provinces/search/{query}` - Search province by name
-- `GET /api/provinces-32/*` - Alternative 32-province variant
-- `GET /api/network/check` - Check connectivity to Planetary Computer
-- `GET /api/cache/info` - Show cache statistics
-- `POST /api/cache/clear` - Clear local cache
-
-### NDVI & Time-Series
-- `POST /api/ndvi/timeseries` - Calculate NDVI time-series
-- `POST /api/ndvi/predict-timeseries` - NDVI prediction/forecast
-- `POST /api/ndvi/forecast` - NDVI forecasting
-
-### File Management
-- `GET /api/training/files` - List training files
-- `GET /api/overlay/shapefiles` - List available shapefiles
-- `GET /api/training/shapefile/{filename}/labels` - Get shapefile labels
-- `POST /api/land-classification/upload` - Upload custom model
-- `POST /api/cloud-removal/upload` - Upload cloud removal model
-
-### Frontend Routes (Serve HTML)
-- `GET /` - Main dashboard
-- `GET /training` - Training interface
-- `GET /prediction` - Prediction interface
-- `GET /dashboard` - Analytics dashboard
-- `GET /batch` - Batch processing UI
-- `GET /ndvi` - NDVI time-series UI
-- `GET /reports` - Reports management
-- `GET /cloud-training` - Cloud removal training
-- `GET /change-detection` - Change detection UI
-
----
-
-## 💾 DATA INPUTS / OUTPUTS & FOLDER CONVENTIONS
-
-### Input Data Sources
-- **Sentinel-2 L2A** from Microsoft Planetary Computer STAC API
-  - Bands: B02 (blue), B03 (green), B04 (red), B08 (NIR), B11 (SWIR), SCL (cloud mask)
-  - Resolution: 10m or 20m (user selectable)
-  - Collection: `sentinel-2-l2a`
-
-- **Sentinel-1 RTC** from Planetary Computer
-  - Bands: VH, VV (radar polarizations)
-  - Converted to dB scale: `10 * log10(intensity)`
-  - Collection: `sentinel-1-rtc`
-
-- **Training Labels**: User-provided shapefiles with pixel-level class labels
-
-### Output File Structure
-```
-predictions/
-  ├── prediction_YYYYMMDD_HHMMSS.tif         # Classification GeoTIFF
-  ├── prediction_YYYYMMDD_HHMMSS.png         # PNG preview
-  ├── ndvi_YYYYMMDD_HHMMSS.tif              # NDVI raster
-  ├── ndvi_YYYYMMDD_HHMMSS.png              # NDVI preview
-
-reports/
-  ├── training_report_*.html                 # Auto training reports
-  ├── prediction_report_*.html                # Auto prediction reports
-
-model_train/
-  ├── model_odc.joblib                      # Legacy model
-  ├── model_odc_info.json                    # Metadata
-  ├── model_xgboost_*.joblib                 # XGBoost models
-  ├── model_xgboost_*_info.json              # Metadata
-  ├── model_cnn_*.joblib                     # CNN models
-  ├── model_cnn_*_info.json                  # Metadata
-
-cloud_removal_model/
-  ├── cloud_removal_unet_best.pth            # Trained U-Net
-  ├── *.pth                                  # Custom models
-  ├── *.json                                 # Model metadata
-```
-
----
-
-## 🔌 EXTERNAL DEPENDENCIES & PLATFORMS
-
-### Critical External Services
-- **Microsoft Planetary Computer** (STAC API)
-  - Hosts Sentinel-2 L2A and Sentinel-1 RTC archives
-  - URL: `https://planetarycomputer.microsoft.com/api/stac/v1`
-  - Auto-signed access tokens via `planetary_computer.sign_inplace`
-  - Network connectivity check: `GET /api/network/check`
-
-### Key Python Libraries
-- **Geospatial**: rasterio, rioxarray, geopandas, shapely, Cartopy, folium, ipyleaflet
-- **Data Processing**: numpy, pandas, xarray, dask
-- **ML**: scikit-learn, xgboost
-- **Deep Learning**: torch, torchvision
-- **Web**: fastapi, uvicorn, pydantic
-- **Visualization**: matplotlib, Pillow (PIL)
-- **Document Gen**: markdown, Pillow
-
-### GPU Support
-- PyTorch with CUDA 12.x (optional; falls back to CPU)
-- Benefits Swin-UNet and CNN models (10-100x speedup)
-- CPU training for XGBoost/RF typically <1 hour; deep models need GPU for reasonable speed
-
----
-
-## ⚙️ FEATURE EXTRACTION MODES (CRITICAL)
-
-Train and prediction **MUST** use same feature mode and dimension; metadata auto-detects this.
-
-| Mode | # Features | Description | Best For | Training Time |
-|---|---|---|---|---|
-| **simple** | 3 | NDVI_mean, VH_db_mean, VV_db_mean | Fast iteration, baseline | ~5-10 min |
-| **temporal** | 39 | NDVI/NDWI/NDBI across 13 months + radar stats | High accuracy (~85%+) | ~30-60 min |
-| **extended** | 15 | NDVI/NDWI/NDBI stats (mean/std/min/max) + radar | Balanced speed/accuracy | ~15-30 min |
-| **odc** | 8 | NDVI stats + NDWI/NDBI/EVI mean (legacy ODC mode) | Legacy compatibility | ~10-20 min |
-
-**Critical**: If feature mode = "temporal" (39 features) at training, prediction MUST extract 39 features. System auto-detects from metadata but will fail if mismatched.
-
----
-
-## 🎯 OPERATIONAL NOTES & CONSTRAINTS
-
-### Performance Limits
-1. **Planetary Computer Timeout Issues**
-   - Large bbox (>10km × 10km) + long time range (>1 month) + high max_scenes → timeouts
-   - **Solution**: Progressive loading (subdivide bbox), reduce time window, reduce max_scenes
-   - **Safe Settings**: bbox ≤ 10km × 10km, time ≤ 1 month, max_scenes ≤ 12
-
-2. **Memory Usage**
-   - Temporal mode (39 features) requires ~2-3x RAM vs simple mode
-   - Large regions: reduce resolution (10m → 20m) or split into sub-tiles
-   - Batch processing: sequential (one region at a time due to API limits)
-
-3. **GPU Training**
-   - Swin-UNet: ~15-60 min on GPU vs ~2-4 hours on CPU
-   - CNN: ~10-30 min on GPU vs ~1-2 hours on CPU
-   - XGBoost/RF: CPU-bound; GPU not beneficial
-
-### Data Quality Issues
-1. **Cloud Cover**
-   - SCL band values: 3=cloud shadow, 8=cloud medium, 9=cloud high, 10=cirrus
-   - Recommend multiple scenes (≥5) for temporal aggregation
-   - Cloud removal strategy critical—test different approaches
-
-2. **Radar Data (Sentinel-1)**
-   - Not always available for all regions/dates
-   - System gracefully falls back to zeros if unavailable
-   - Safe for "extended" & "odc" modes that have radar fallback
-
-3. **Feature Mode Mismatch**
-   - Model trained with "temporal" (39 features) needs 39-dim input
-   - System auto-adjusts (pads/trims) from metadata but may degrade accuracy
-   - **Best Practice**: Align feature mode explicitly; don't mix
-
-### Known Caveats
-1. **Legacy Model (model_odc.joblib)**: Hardcoded 39 temporal features; auto-detected via `model_odc_info.json`
-2. **Metadata Consistency**: Old models may lack `.json` sidecar; system generates default (may be incorrect)
-3. **Batch Processing**: Sequential only; large batches (100+ regions) take hours
-4. **Change Detection**: Simple differencing approach; requires same model & feature mode for both periods
-5. **Rate Limiting**: Planetary Computer may rate-limit if too many concurrent requests
-
-### Recommended Best Practices
-- Test model on small bbox first (2km × 2km, 1 week, 3 scenes)
-- Use "simple" mode for fast iteration, "temporal" for best accuracy (85%+)
-- Store metadata JSON alongside model file (sidecar pattern)
-- Version control: record feature_mode & n_features in every training
-- Monitor training accuracy; retrain if <70% accuracy
-- Cache Sentinel data locally to avoid repeated downloads
-- Use "median_composite" cloud strategy if >5 scenes; "temporal_only" if 3-4 scenes
-
----
-
-## 🔍 TEST COVERAGE MAP
-
-| Test File | Coverage | Status |
-|---|---|---|
-| `test_model_manager.py` | ModelManager lifecycle (list, load, validate) | ✅ Well-tested |
-| `test_feature_extractor.py` | All 4 feature extraction modes | ✅ Well-tested |
-| `test_training_api.py` | Training API endpoints | ✅ Partial |
-| `test_cloud_removal.py` | 7 cloud removal strategies | ✅ Well-tested |
-| `test_cloud_training.py` | U-Net cloud removal training | ✅ Partial |
-| `test_shapefile_api.py` | Shapefile overlay feature | ✅ Partial |
-| `test_planetary_computer.py` | Planetary Computer STAC access | ✅ Well-tested |
-| `test_new_features.py` | Recent feature releases | ✅ Partial |
-| Jupyter Notebooks | Training & prediction workflows | ✅ Mix of unit/integration/notebooks |
-
-**Coverage Notes**: Model management, feature extraction, and cloud removal well-tested; Dashboard UI, change detection, NDVI time-series mostly tested via notebooks.
-
----
-
-## 📚 FILE REFERENCE MAP
-
-### Core Execution
-- `api_server.py` — Main FastAPI application (~4200 LOC)
-- `train_module.py` — Training logic (data fetch → feature extraction → training)
-- `run_prediction_new.py` — Prediction execution function
-- `feature_extractor.py` — Unified feature extraction (4 modes)
-- `model_manager.py` — Model lifecycle (load/save/validate/list)
-- `cloud_removal.py` — Cloud removal strategies (7 methods)
-- `report_generator.py` — HTML/PNG report auto-generation
-- `generate_previews.py` — GeoTIFF → PNG conversion
-
-### Data & Config
-- `vietnam_provinces.py` — 32+ province lookup tables & bboxes
-- `vietnam_provinces_merged.py` — Alternative 32-province variant
-- `utils.py` — Geospatial utility functions
-- `create_odc_metadata.py` — Legacy metadata generator
-
-### Frontend
-- `index.html` — Main dashboard hub (tab navigation)
-- `training_interface.html` — Training configuration UI
-- `prediction_interface.html` — Prediction configuration UI
-- `batch_interface.html` — Batch processing (CSV upload)
-- `ndvi_interface.html` — NDVI time-series visualization
-- `dashboard.html` — Analytics & model performance dashboard
-- `reports_interface.html` — Report management & viewing
-- `change_detection_interface.html` — Change detection visualization
-- `cloud_training_interface.html` — Cloud removal U-Net training
-
-### Documentation
-- `IMPLEMENTATION_SUMMARY.md` — Model manager & system overview
-- `MODEL_MANAGER_GUIDE.md` — Complete model management guide
-- `NDVI_FORECAST_METHODOLOGY.md` — NDVI algorithm documentation
-- `CLOUD_TRAINING_GUIDE.md` — Cloud removal training guide
-- `NDVI_PREDICTION_GUIDE.md` — NDVI prediction workflow
-- `CLOUD_PROCESSING.md` — Cloud processing notes
-- `UPDATE_SUMMARY.md` — Recent updates & features
-
----
-
-## 🚀 BOOTSTRAP PROMPT FOR GEMINI
-
-### System Context (Copy & Paste for Gemini)
-
-```
-You are assisting a remote-sensing land-classification project for Vietnam.
-
-## ARCHITECTURE SNAPSHOT
-- **Backend**: FastAPI (~4200 LOC, 70+ endpoints) for orchestrating training, prediction, batch, cloud removal, reporting
-- **Data Source**: Microsoft Planetary Computer STAC API (Sentinel-2 L2A + Sentinel-1 RTC)
-- **Training**: scikit-learn (XGBoost/RF/SVM/DT) + PyTorch (CNN/Swin-UNet/MobileNet)
-- **Feature Extraction**: 4 modes (simple 3-feat / temporal 39-feat / extended 15-feat / odc 8-feat)
-- **Cloud Removal**: 7 strategies (classic, temporal_only, median_composite, none, ML inpainting, deep U-Net)
-- **Output**: GeoTIFF + PNG + HTML report + JSON metadata
-
-## CORE FILES TO UNDERSTAND (Priority Order)
-1. api_server.py — Main API server (training, prediction, batch, models, reports)
-2. train_module.py — Training pipeline (data fetch → feature extraction → train → save)
-3. feature_extractor.py — Unified feature extraction with auto mode detection
-4. model_manager.py — Model lifecycle (load/save/validate/list)
-5. cloud_removal.py — Cloud removal strategies (7 methods)
-6. report_generator.py — Auto-generate HTML reports
-7. run_prediction_new.py — Prediction execution
-8. vietnam_provinces.py — Province lookup & bbox tables
-
-## CRITICAL CONSTRAINTS & GOTCHAS
-1. **Feature Mode Consistency**: Training & prediction MUST use same mode (simple/temporal/extended/odc)
-   → Auto-detected from metadata JSON
-   → Mismatch causes dimension error or accuracy degradation
-
-2. **Planetary Computer Limits**:
-   → Timeout if bbox >10km×10km OR time range >1 month OR max_scenes >12
-   → Solution: subdivide bbox, reduce time window, limit scenes
-
-3. **Cloud Strategy Selection**:
-   → ≥5 scenes → use "median_composite" (best noise reduction)
-   → 3-4 scenes → use "temporal_only" (fast temporal interp)
-   → <3 scenes → use "none" (skip cloud removal)
-
-4. **Radar Data Fallback**:
-   → Sentinel-1 may be unavailable for some regions
-   → System gracefully falls back to zeros (safe for all modes)
-
-5. **Model Metadata**:
-   → Always stored as `model_name_info.json` sidecar file
-   → Contains: n_features, feature_mode, features list, accuracy, bbox, time_range
-   → Missing metadata → system uses defaults (may be incorrect)
-
-6. **Legacy Model (model_odc.joblib)**:
-   → Hardcoded 39 temporal features
-   → Metadata in model_odc_info.json
-
-## REASONING CHECKLIST (before answering)
-□ Is feature_mode consistent between train and prediction?
-□ Is metadata.json present and correct?
-□ Does bbox exceed 10km×10km? (Planetary Computer timeout risk)
-□ Is cloud_removal_strategy appropriate for # of scenes?
-□ Is Sentinel-1 data available for this region/date?
-□ Is model a joblib (scikit-learn) or .pth (PyTorch) file?
-□ Is GPU available for deep models (CNN, Swin-UNet)?
-□ Does memory allow temporal feature extraction (39-feat)?
-
-## RESPONSE FORMAT
-- Always cite api_server.py endpoint, function name, or module being discussed
-- Verify feature_mode & n_features from metadata JSON
-- Suggest cloud_removal_strategy based on # of scenes available
-- For unknown issues: offer alternative approaches (reduce bbox, cache results, use simpler model)
-- Explain reasoning using checklist above
-
-## DATA FLOW SUMMARY
-Sentinel-2/S1 → [Cloud Remove] → [Feature Extract] → [Train/Predict] → [GeoTIFF + PNG + Report]
-```
-
----
-
-## 📞 QUICK REFERENCE CHECKLIST
-
-### Before Troubleshooting Any Issue
-- [ ] Check feature_mode consistency (metadata JSON)
-- [ ] Verify metadata.json exists for the model
-- [ ] Check Planetary Computer connectivity (`GET /api/network/check`)
-- [ ] Review cloud_removal_method choice (≥5 scenes = median_composite)
-- [ ] Confirm Sentinel-1 availability (or fallback to zeros if missing)
-- [ ] Validate bbox size (≤10km×10km for safety)
-- [ ] Check memory usage for temporal feature mode
-- [ ] Verify GPU if using CNN/Swin-UNet models
-
-### Common Issues & Solutions
-| Issue | Likely Cause | Solution |
-|---|---|---|
-| Training timeout | Large bbox / long time / many scenes | Subdivide bbox, reduce time window, max_scenes ≤ 12 |
-| Feature dimension mismatch | Different feature_mode between train & predict | Check metadata.json, ensure same mode |
-| Low prediction accuracy | Cloud cover, poor training data, feature mode too simple | Use "temporal" mode, increase training data, try cloud removal |
-| Out of memory | Temporal features + large region | Reduce resolution (20m), split into sub-tiles, increase RAM |
-| Model not found | Wrong filename or model_train/ path issue | `GET /api/models/list` to verify, check file path |
-| Planetary Computer error | Network issue or API rate limit | Check DNS, retry later, reduce concurrent requests |
-| Cloud removal failing | Strategy not suitable for scene count | Try "none" or "median_composite" depending on scenes |
-
----
-
-## 🎓 LEARNING RESOURCES IN REPO
-
-- **Notebooks**: `01.train_ODC.ipynb`, `02.predict_ODC.ipynb`, `cloud_removal_train.ipynb`
-- **Tests**: `test_*.py` files for unit test patterns
-- **Docs**: All `*.md` files for detailed guides and methodology
-- **Code Comments**: API server and modules heavily commented
-
----
-
-**Generated**: March 26, 2026  
-**For Use By**: Gemini, Claude, GPT, or any AI system needing project context  
-**Maintainer**: Remote-Sensing Project Team
-

IMPLEMENTATION_SUMMARY.md

@@ -1,235 +0,0 @@
-# Hệ Thống Model Manager - Tóm Tắt Triển Khai
-
-## ✅ Đã Hoàn Thành
-
-### 1. **Model Manager Core System** (`model_manager.py`)
-Tạo class `ModelManager` với đầy đủ chức năng:
-
-- ✅ **List Models**: Liệt kê tất cả models với metadata
-- ✅ **Load Model**: Load model + metadata + label encoder
-- ✅ **Save Model**: Lưu model kèm metadata tự động
-- ✅ **Validate Model**: Kiểm tra tính hợp lệ của model
-- ✅ **Get Features**: Lấy danh sách features cần thiết
-- ✅ **Delete Model**: Xóa model và metadata
-- ✅ **Get Latest**: Tìm model mới nhất (theo type)
-- ✅ **Auto-detect**: Tự động phát hiện CNN/PyTorch models
-
-### 2. **API Integration** (`api_server.py`)
-Tích hợp ModelManager vào tất cả prediction endpoints:
-
-- ✅ `GET /api/models/list` - List tất cả models
-- ✅ `GET /api/models/{filename}/info` - Chi tiết model
-- ✅ `GET /api/models/{filename}/validate` - Validate model
-- ✅ `DELETE /api/models/{filename}` - Xóa model
-- ✅ Updated `POST /api/predict` - Sử dụng ModelManager
-- ✅ Updated `POST /api/batch/predict` - Batch với ModelManager
-- ✅ Updated `POST /api/predict-with-ndvi` - NDVI + ModelManager
-- ✅ Updated Change Detection - Với ModelManager
-
-### 3. **Training Integration** (`train_module.py`, `new_import_ODC.py`)
-Cập nhật training code để tự động save metadata:
-
-- ✅ `train_module.py`: Sử dụng ModelManager khi save model
-- ✅ `new_import_ODC.py`: Updated `save_model()` function
-- ✅ Tự động tạo metadata khi train model mới
-- ✅ Backward compatible với old format
-
-### 4. **Bug Fixes**
-- ✅ Fixed `NameError: is_cnn_model not defined`
-- ✅ Fixed feature mismatch (39 features vs 3 features)
-- ✅ Added temporal feature extraction logic
-- ✅ Auto-adjust features to match model requirements
-
-### 5. **Legacy Support**
-- ✅ Tạo metadata cho `model_odc.joblib` 
-- ✅ Support models không có metadata (tạo default)
-- ✅ Backward compatible với old model format
-
-### 6. **Documentation & Testing**
-- ✅ `MODEL_MANAGER_GUIDE.md` - Hướng dẫn đầy đủ
-- ✅ `test_model_manager.py` - Test suite
-- ✅ `create_odc_metadata.py` - Utility script
-
-## 🎯 Các Tính Năng Chính
-
-### Automatic Feature Detection
-Hệ thống tự động:
-- Detect số features cần thiết từ metadata
-- Extract đúng features (temporal hoặc aggregate)
-- Adjust features để match với model (pad/trim)
-
-### Multi-Model Support
-Hỗ trợ tất cả các loại models:
-- ✅ **XGBoost**: GPU-accelerated gradient boosting
-- ✅ **Random Forest**: Ensemble learning  
-- ✅ **Decision Tree**: Simple tree-based
-- ✅ **SVM**: Support Vector Machine
-- ✅ **CNN**: PyTorch neural networks
-- ✅ **Custom models**: Bất kỳ scikit-learn compatible model
-
-### Intelligent Feature Extraction
-
-```python
-# Tự động detect và extract features dựa vào metadata
-if expected_n_features > 10:
-    # Temporal features (all time steps)
-    features = [ndvi_t1, ndvi_t2, ..., ndwi_t1, ndwi_t2, ...]
-else:
-    # Aggregate features (mean values)
-    features = [ndvi_mean, ndwi_mean, ndbi_mean]
-```
-
-## 📊 Model Metadata Format
-
-```json
-{
-  "timestamp": "2025-12-21T17:23:57",
-  "model_type": "xgboost",
-  "features": ["NDVI_mean", "VH_dB_mean", "VV_dB_mean"],
-  "n_features": 3,
-  "n_classes": 7,
-  "test_accuracy": 0.578125,
-  "train_accuracy": 1.0,
-  "data_source": "Microsoft Planetary Computer STAC",
-  "collections": ["sentinel-2-l2a", "sentinel-1-rtc"],
-  "bbox": [105.6, 9.3, 106.2, 9.8],
-  "time_range": "2023-03-01/2023-05-31",
-  "resolution": 20
-}
-```
-
-## 🔄 Workflow
-
-### Training → Saving
-```python
-# Train model
-model = XGBClassifier()
-model.fit(X_train, y_train)
-
-# Prepare metadata
-metadata = {
-    "model_type": "xgboost",
-    "features": ["NDVI_mean", "VH_dB_mean", "VV_dB_mean"],
-    "n_features": 3,
-    "test_accuracy": accuracy_score(y_test, y_pred)
-}
-
-# Save with ModelManager
-model_manager.save_model(model, metadata, label_encoder=encoder)
-```
-
-### Loading → Predicting
-```python
-# Load model
-model_manager = get_model_manager()
-model, encoder, metadata = model_manager.load_model("model_xgb.joblib")
-
-# Get required features
-required_features = metadata["features"]
-n_features = metadata["n_features"]
-
-# Extract features
-features = extract_features(data, required_features)
-
-# Predict
-predictions = model.predict(features)
-```
-
-## 📂 File Structure
-
-```
-remote-sensing/
-├── model_manager.py              # Core ModelManager class
-├── api_server.py                 # API với ModelManager integration  
-├── train_module.py               # Training với auto-save metadata
-├── new_import_ODC.py             # Updated save_model function
-├── test_model_manager.py         # Test suite
-├── create_odc_metadata.py        # Metadata generator
-├── MODEL_MANAGER_GUIDE.md        # Full documentation
-└── model_train/
-    ├── model_odc.joblib          # Legacy model
-    ├── model_odc_info.json       # Metadata (created)
-    ├── model_xgboost_*.joblib    # New models
-    ├── model_xgboost_*_info.json # Auto-generated metadata
-    ├── model_cnn_*.joblib
-    └── model_cnn_*_info.json
-```
-
-## 🚀 Usage Examples
-
-### API - List Models
-```bash
-curl http://localhost:8000/api/models/list
-```
-
-Response:
-```json
-{
-  "success": true,
-  "models": [
-    {
-      "filename": "model_xgboost_20251221_172351.joblib",
-      "model_type": "xgboost",
-      "n_features": 3,
-      "test_accuracy": 0.578125,
-      "size_mb": 0.45
-    }
-  ]
-}
-```
-
-### API - Predict with Specific Model
-```bash
-curl -X POST http://localhost:8000/api/predict \
-  -H "Content-Type: application/json" \
-  -d '{
-    "model_filename": "model_xgboost_20251221_172351.joblib",
-    "min_lon": 105.6,
-    "max_lon": 106.2,
-    "start_date": "2023-03-01",
-    "end_date": "2023-05-31"
-  }'
-```
-
-### Python - Use ModelManager
-```python
-from model_manager import get_model_manager
-
-# List all models
-mm = get_model_manager()
-models = mm.list_models()
-
-# Load specific model
-model, encoder, metadata = mm.load_model("model_odc.joblib")
-
-# Validate
-validation = mm.validate_model("model_odc.joblib")
-print(validation['valid'])  # True/False
-```
-
-## 🔧 Key Improvements
-
-1. **Centralized Model Management**: Một nơi quản lý tất cả models
-2. **Automatic Feature Detection**: Không cần hardcode features
-3. **Metadata Driven**: Models tự document mình
-4. **Multi-Model Ready**: Dễ dàng switch giữa các models
-5. **Backward Compatible**: Vẫn support old models
-6. **Error Handling**: Validate và report lỗi rõ ràng
-
-## 🎉 Kết Quả
-
-Hệ thống bây giờ có thể:
-- ✅ Vận hành với **TẤT CẢ** các models (XGBoost, CNN, RF, SVM, etc.)
-- ✅ Tự động detect và extract đúng features
-- ✅ List, load, validate, delete models qua API
-- ✅ Support cả legacy models (model_odc.joblib)
-- ✅ Training tự động save metadata
-- ✅ Prediction tự động adjust features
-
-## 🔜 Next Steps (Optional)
-
-1. **Model Versioning**: Track model versions
-2. **Model Comparison**: So sánh performance nhiều models
-3. **Auto Model Selection**: Chọn model tốt nhất tự động
-4. **Model Ensemble**: Combine predictions từ nhiều models
-5. **Model Monitoring**: Track prediction quality over time

KetQuaPhanLoaiDat.tif

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ff33ab950e3524342a99e70a02598ccb1ca57e60627c887c10b34466eabf3e87
+size 3536032

MODEL_MANAGER_GUIDE.md

@@ -1,347 +0,0 @@
-# Hệ Thống Quản Lý Model - Model Manager
-
-## Tổng quan
-
-Hệ thống **Model Manager** cho phép vận hành và quản lý tất cả các loại models trong dự án Land Classification, bao gồm:
-- XGBoost
-- Random Forest
-- Decision Tree
-- SVM
-- CNN (PyTorch)
-- Các model khác
-
-## Cấu trúc
-
-### 1. Model Storage
-```
-model_train/
-├── model_odc.joblib                    # Model file
-├── model_xgboost_20251221_172351.joblib
-├── model_xgboost_20251221_172351_info.json  # Metadata
-├── model_cnn_20251221_163841.joblib
-└── model_cnn_20251221_163841_info.json
-```
-
-### 2. Metadata Format
-Mỗi model đi kèm với file JSON chứa metadata:
-
-```json
-{
-  "timestamp": "2025-12-21T17:23:57.306042",
-  "data_source": "Microsoft Planetary Computer STAC",
-  "collections": ["sentinel-2-l2a", "sentinel-1-rtc"],
-  "features": ["NDVI_mean", "VH_dB_mean", "VV_dB_mean"],
-  "model_type": "xgboost",
-  "n_features": 3,
-  "n_classes": 7,
-  "test_accuracy": 0.578125,
-  "train_accuracy": 1.0,
-  "classification_report": {...},
-  "confusion_matrix": [...],
-  "bbox": [105.6, 9.3, 106.2, 9.8],
-  "time_range": "2023-03-01/2023-05-31",
-  "resolution": 20
-}
-```
-
-## Sử dụng
-
-### 1. Trong Python Code
-
-#### List tất cả models
-```python
-from model_manager import get_model_manager
-
-model_manager = get_model_manager()
-models = model_manager.list_models()
-
-for model in models:
-    print(f"{model['filename']} - {model['model_type']} - Accuracy: {model['test_accuracy']}")
-```
-
-#### Load model
-```python
-model, encoder, metadata = model_manager.load_model("model_xgboost_20251221_172351.joblib")
-
-print(f"Model type: {metadata['model_type']}")
-print(f"Required features: {metadata['features']}")
-```
-
-#### Save model mới
-```python
-metadata = {
-    "timestamp": datetime.now().isoformat(),
-    "model_type": "random_forest",
-    "features": ["NDVI_mean", "VH_dB_mean", "VV_dB_mean"],
-    "n_features": 3,
-    "n_classes": 7,
-    "test_accuracy": 0.85,
-    "train_accuracy": 0.95
-}
-
-model_manager.save_model(
-    model=trained_model,
-    metadata=metadata,
-    model_filename="my_model.joblib",
-    label_encoder=encoder
-)
-```
-
-#### Validate model
-```python
-validation = model_manager.validate_model("model_odc.joblib")
-print(f"Valid: {validation['valid']}")
-print(f"Errors: {validation['errors']}")
-print(f"Warnings: {validation['warnings']}")
-```
-
-#### Get required features
-```python
-features = model_manager.get_required_features("model_xgboost_20251221_172351.joblib")
-print(f"Required features: {features}")
-```
-
-### 2. Trong Notebook Training
-
-File `01.train_ODC.ipynb` hoặc các notebook khác:
-
-```python
-# Import
-from new_import_ODC import save_model
-
-# Train model
-model = RandomForestClassifier(n_estimators=100)
-model.fit(X_train, y_train)
-
-# Prepare metadata
-metadata = {
-    "timestamp": datetime.now().isoformat(),
-    "model_type": "random_forest",
-    "features": ["ndvi"],  # Danh sách features đã dùng
-    "n_features": 1,
-    "n_classes": len(np.unique(y_train)),
-    "test_accuracy": accuracy_score(y_test, y_pred),
-    "train_accuracy": model.score(X_train, y_train),
-    "data_source": "Local S3 ODC",
-    "training_samples": len(X_train),
-    "testing_samples": len(X_test)
-}
-
-# Save với metadata
-save_model("model_odc.joblib", model, metadata=metadata, label_encoder=None)
-```
-
-### 3. Qua API
-
-#### List models
-```bash
-curl http://localhost:8000/api/models/list
-```
-
-Response:
-```json
-{
-  "success": true,
-  "models": [
-    {
-      "filename": "model_xgboost_20251221_172351.joblib",
-      "model_type": "xgboost",
-      "features": ["NDVI_mean", "VH_dB_mean", "VV_dB_mean"],
-      "test_accuracy": 0.578125,
-      "size_mb": 0.45
-    }
-  ],
-  "count": 3
-}
-```
-
-#### Get model info
-```bash
-curl http://localhost:8000/api/models/model_odc.joblib/info
-```
-
-#### Validate model
-```bash
-curl http://localhost:8000/api/models/model_odc.joblib/validate
-```
-
-#### Delete model
-```bash
-curl -X DELETE http://localhost:8000/api/models/old_model.joblib
-```
-
-#### Predict với model cụ thể
-```bash
-curl -X POST http://localhost:8000/api/predict \
-  -H "Content-Type: application/json" \
-  -d '{
-    "model_filename": "model_xgboost_20251221_172351.joblib",
-    "min_lon": 105.6,
-    "min_lat": 9.3,
-    "max_lon": 106.2,
-    "max_lat": 9.8,
-    "start_date": "2023-03-01",
-    "end_date": "2023-05-31"
-  }'
-```
-
-## Features Chính
-
-### 1. Automatic Feature Detection
-Hệ thống tự động detect features cần thiết từ metadata:
-```python
-metadata = model_manager._load_metadata("model.joblib")
-required_features = metadata.get("features", [])
-```
-
-### 2. Model Type Support
-Hỗ trợ nhiều loại model:
-- **XGBoost**: GPU-accelerated gradient boosting
-- **Random Forest**: Ensemble learning
-- **Decision Tree**: Simple tree-based
-- **SVM**: Support Vector Machine
-- **CNN**: PyTorch neural networks
-
-### 3. Backward Compatibility
-Hệ thống vẫn hỗ trợ models cũ không có metadata:
-- Tự động detect và tạo default metadata
-- Load được cả format cũ (model only) và mới (dict với encoder)
-
-### 4. Validation
-Kiểm tra tính hợp lệ của model:
-- File tồn tại
-- Load được
-- Metadata đầy đủ
-- Features requirements
-
-## Testing
-
-Chạy test suite:
-```bash
-python test_model_manager.py
-```
-
-Output mẫu:
-```
-======================================================================
-MODEL MANAGER TEST
-======================================================================
-
-✅ ModelManager initialized
-
-======================================================================
-TEST 1: LIST ALL MODELS
-======================================================================
-
-📦 Found 3 models:
-
-[1] model_xgboost_20251221_172351.joblib
-    Size: 0.45 MB
-    Type: xgboost
-    Features: 3
-    Accuracy: 0.578125
-
-[2] model_cnn_20251221_163841.joblib
-    Size: 0.12 MB
-    Type: cnn
-    Features: 3
-    Accuracy: 0.507812
-
-[3] model_odc.joblib
-    Size: 0.02 MB
-    ⚠️  No metadata
-```
-
-## Migration Guide
-
-### Cho Models Cũ
-
-Nếu bạn có models cũ không có metadata, có 2 cách:
-
-#### Option 1: Tự động (Recommended)
-Hệ thống sẽ tự động tạo default metadata khi load
-
-#### Option 2: Tạo metadata manually
-```python
-# Tạo metadata file
-metadata = {
-    "timestamp": "2025-12-21T12:00:00",
-    "model_type": "random_forest",  # hoặc model type tương ứng
-    "features": ["ndvi"],  # Features đã dùng khi train
-    "n_features": 1,
-    "n_classes": 8,
-    "test_accuracy": 0.75,  # Nếu biết
-}
-
-import json
-with open("model_train/model_odc_info.json", "w") as f:
-    json.dump(metadata, f, indent=2)
-```
-
-### Cho Training Code Mới
-
-Luôn save model với metadata:
-```python
-save_model(
-    name_file="my_model.joblib",
-    model=trained_model,
-    metadata={...},  # Bắt buộc
-    label_encoder=encoder
-)
-```
-
-## Best Practices
-
-1. **Luôn include metadata** khi save model mới
-2. **Sử dụng naming convention**: `model_{type}_{timestamp}.joblib`
-3. **Test model** sau khi train: `model_manager.validate_model()`
-4. **Document features** trong metadata để dễ sử dụng sau này
-5. **Backup models** quan trọng trước khi xóa
-
-## Troubleshooting
-
-### Model không load được
-```python
-validation = model_manager.validate_model("model.joblib")
-print(validation['errors'])  # Xem lỗi cụ thể
-```
-
-### Thiếu metadata
-Tạo metadata file manually (xem Migration Guide)
-
-### Features không khớp
-Kiểm tra `metadata['features']` và đảm bảo data đầu vào có đúng features
-
-## API Endpoints Summary
-
-| Endpoint | Method | Description |
-|----------|--------|-------------|
-| `/api/models/list` | GET | List all models |
-| `/api/models/{filename}/info` | GET | Get model details |
-| `/api/models/{filename}/validate` | GET | Validate model |
-| `/api/models/{filename}` | DELETE | Delete model |
-| `/api/predict` | POST | Predict with model |
-| `/api/batch/predict` | POST | Batch prediction |
-| `/api/predict-with-ndvi` | POST | Predict + NDVI export |
-
-## File Structure
-
-```
-remote-sensing/
-├── model_manager.py           # Core ModelManager class
-├── test_model_manager.py      # Test suite
-├── new_import_ODC.py          # Updated save_model function
-├── train_module.py            # Updated training module
-├── api_server.py              # API với ModelManager integration
-└── model_train/               # Models directory
-    ├── *.joblib               # Model files
-    └── *_info.json            # Metadata files
-```
-
-## Next Steps
-
-1. ✅ Migrate existing notebooks để sử dụng metadata
-2. ✅ Update UI để cho phép chọn model
-3. ✅ Add model comparison features
-4. ✅ Implement model versioning
-5. ✅ Add automated model backup

MODEL_UPLOAD_GUIDE.md

@@ -1,284 +0,0 @@
-# Model Upload Guide
-
-## Overview
-This system now supports uploading custom models for both **Cloud Removal** and **Land Classification** tasks with full metadata tracking.
-
-## Directory Structure
-
-```
-remote-sensing/
-├── cloud_removal_model/          # Cloud removal models (U-Net, GAN, etc.)
-│   ├── *.pth                     # PyTorch model files
-│   └── *.json                    # Metadata sidecar files
-├── land_classification_model/    # Land use classification models
-│   ├── *.pth, *.pkl, *.joblib   # Model files (various formats)
-│   ├── *.h5, *.keras            # TensorFlow/Keras models
-│   └── *.json                    # Metadata sidecar files
-└── model_train/                  # Legacy training outputs (other models)
-```
-
-## Cloud Removal Model Upload
-
-### Supported Format
-- **File Extension**: `.pth` (PyTorch)
-- **Use Case**: Remove clouds from Sentinel-2 imagery
-
-### Metadata Fields
-- **Epoch** (int): Training epoch number
-- **Validation Loss** (float): Best validation loss achieved
-- **Training Loss** (float): Final training loss
-- **Input Channels** (int): Number of input channels (e.g., 6 for S2+S1)
-- **Output Channels** (int): Number of output channels (e.g., 4 for RGBN)
-- **Use Sentinel-1** (bool): Whether model uses SAR data
-- **Description** (string): Optional notes about the model
-
-### API Endpoint
-```http
-POST /api/cloud-removal/upload
-Content-Type: multipart/form-data
-
-{
-  "file": <binary>,
-  "epoch": 50,
-  "val_loss": 0.0134,
-  "train_loss": 0.0142,
-  "in_channels": 6,
-  "out_channels": 4,
-  "use_s1": true,
-  "description": "Trained on winter dataset"
-}
-```
-
-### Example Metadata File
-`cloud_removal_unet_winter.pth.json`:
-```json
-{
-  "filename": "cloud_removal_unet_winter.pth",
-  "epoch": 50,
-  "train_loss": 0.0142,
-  "val_loss": 0.0134,
-  "in_channels": 6,
-  "out_channels": 4,
-  "use_s1": true,
-  "description": "Trained on winter dataset, 50 epochs",
-  "uploaded_at": "2026-01-26T15:30:00"
-}
-```
-
----
-
-## Land Classification Model Upload
-
-### Supported Formats
-- **PyTorch**: `.pth`
-- **Scikit-learn**: `.pkl`, `.joblib`
-- **TensorFlow/Keras**: `.h5`, `.keras`
-
-### Metadata Fields
-- **Model Type**: `mobilenet`, `cnn`, `swin`, `xgboost`, `random_forest`, `other`
-- **Epoch** (int): Training epochs
-- **Train Accuracy** (float %): Training accuracy percentage
-- **Val Accuracy** (float %): Validation accuracy percentage
-- **Train Loss** (float): Final training loss
-- **Val Loss** (float): Final validation loss
-- **Number of Classes** (int): Number of land use classes (e.g., 10)
-- **Input Size** (int): Input image dimension (e.g., 64x64)
-- **Description** (string): Optional notes
-
-### API Endpoint
-```http
-POST /api/land-classification/upload
-Content-Type: multipart/form-data
-
-{
-  "file": <binary>,
-  "model_type": "mobilenet",
-  "epoch": 100,
-  "train_accuracy": 95.5,
-  "val_accuracy": 93.2,
-  "train_loss": 0.12,
-  "val_loss": 0.18,
-  "num_classes": 10,
-  "input_size": 64,
-  "description": "MobileNetV2 trained on Mekong Delta"
-}
-```
-
-### Example Metadata File
-`mobilenet_mekong_v2.pth.json`:
-```json
-{
-  "filename": "mobilenet_mekong_v2.pth",
-  "model_type": "mobilenet",
-  "epoch": 100,
-  "train_accuracy": 95.5,
-  "val_accuracy": 93.2,
-  "train_loss": 0.12,
-  "val_loss": 0.18,
-  "num_classes": 10,
-  "input_size": 64,
-  "description": "MobileNetV2 trained on Mekong Delta dataset",
-  "uploaded_at": "2026-01-26T15:45:00"
-}
-```
-
----
-
-## Usage in Web Interface
-
-### Cloud Removal Models
-1. Navigate to **Prediction Interface**
-2. Select **Cloud Removal Method** → "Deep Learning (U-Net)"
-3. Click **📤 Upload Cloud Removal Model (.pth)**
-4. Fill in metadata form
-5. Click **✅ Upload with Metadata**
-6. Model appears in dropdown with epoch/loss info
-
-### Land Classification Models
-1. Navigate to **Prediction Interface**
-2. In **Model Selection** section
-3. Click **📤 Upload Land Classification Model**
-4. Fill in metadata form (model type, accuracy, etc.)
-5. Click **✅ Upload with Metadata**
-6. Model appears in main model dropdown
-
----
-
-## API Reference
-
-### List Models
-
-**Cloud Removal:**
-```http
-GET /api/cloud-removal/models
-```
-
-**Land Classification:**
-```http
-GET /api/land-classification/models
-```
-
-**Response:**
-```json
-{
-  "models": [
-    {
-      "filename": "model.pth",
-      "epoch": 50,
-      "val_loss": 0.0134,
-      "size_mb": 356.2,
-      "has_metadata": true,
-      "created": 1706284800
-    }
-  ],
-  "count": 1
-}
-```
-
-### Delete Model
-
-**Cloud Removal:**
-```http
-DELETE /api/cloud-removal/models/{filename}
-```
-
-**Land Classification:**
-```http
-DELETE /api/land-classification/models/{filename}
-```
-
----
-
-## Best Practices
-
-1. **Naming Convention**: Use descriptive names
-   - ✅ `cloud_removal_unet_winter_50ep.pth`
-   - ✅ `mobilenet_v2_mekong_acc93.pth`
-   - ❌ `model1.pth`
-
-2. **Metadata Accuracy**: Always fill in actual training metrics
-   - Helps compare model performance
-   - Enables informed model selection
-
-3. **Version Control**: Include version/date in description
-   - "v2.0 - Improved augmentation"
-   - "2026-01-15 - Fixed class imbalance"
-
-4. **File Size**: Monitor model sizes
-   - Cloud removal models: 50-500 MB typical
-   - Land classification: 5-200 MB typical
-   - Large models may require more GPU memory
-
-5. **Testing**: Always test uploaded model on small region first
-   - Verify predictions are reasonable
-   - Check for errors/crashes
-
----
-
-## Troubleshooting
-
-### Upload Fails with "Already Exists"
-- Model filename is duplicate
-- Delete old model first or rename new one
-
-### Model Shows Default Values (0, 0, 0)
-- Server needs restart to load `Form(...)` imports
-- Refresh page and try again
-
-### Model Not Appearing in Dropdown
-- Click **🔄 Refresh** button
-- Check file extension is valid
-- Verify model saved to correct folder
-
-### Metadata Not Displaying
-- Check `.json` file exists alongside model
-- Verify JSON format is valid
-- Look for server errors in terminal
-
----
-
-## Migration from Old System
-
-If you have models in `model_train/`:
-
-1. **Cloud Removal Models**: Move to `cloud_removal_model/`
-   ```bash
-   mv model_train/cloud_removal_*.pth cloud_removal_model/
-   mv model_train/*_unet*.pth cloud_removal_model/
-   mv model_train/*GAN*.pth cloud_removal_model/
-   ```
-
-2. **Land Classification Models**: Move to `land_classification_model/`
-   ```bash
-   mv model_train/mobilenet*.pth land_classification_model/
-   mv model_train/cnn*.pth land_classification_model/
-   mv model_train/swin*.pth land_classification_model/
-   mv model_train/*.pkl land_classification_model/
-   ```
-
-3. **Create metadata files** by re-uploading through web interface
-
----
-
-## Security Features
-
-✅ **File Extension Validation**: Only allowed formats accepted  
-✅ **Path Traversal Prevention**: No `../` or `/` in filenames  
-✅ **Duplicate Detection**: Prevents overwriting existing models  
-✅ **Size Limits**: Prevents extremely large uploads  
-✅ **JSON Sanitization**: Metadata stored safely
-
----
-
-## Future Enhancements
-
-- [ ] Batch model upload
-- [ ] Model versioning system
-- [ ] Automated benchmarking
-- [ ] Model comparison tool
-- [ ] Export/import model configs
-- [ ] Cloud storage integration
-
----
-
-**Last Updated**: January 26, 2026

NDVI_FORECAST_METHODOLOGY.md

@@ -1,737 +0,0 @@
-# NDVI Time Series Forecasting Methodology
-## Land-Type-Specific Seasonal Forecasting
-
-**Date:** January 4, 2026  
-**Author:** Remote Sensing Analysis System  
-**Version:** 1.0
-
----
-
-## 1. Tổng Quan (Overview)
-
-### 1.1 Mục Tiêu
-Dự đoán chỉ số thực vật NDVI (Normalized Difference Vegetation Index) và các spectral indices khác (NDWI, NDBI, EVI) cho thời gian tương lai dựa trên:
-- **Input:** Tọa độ địa lý (bbox) + Khoảng thời gian tương lai
-- **Output:** 8 giá trị time series (ndvi_mean, ndvi_min, ndvi_max, ndvi_std, ndvi_range, ndwi_mean, ndbi_mean, evi_mean)
-
-### 1.2 Thách Thức
-- Không có dữ liệu vệ tinh Sentinel-2 cho tương lai
-- Pattern NDVI khác nhau đáng kể giữa các loại đất:
-  - **Lúa nước:** NDVI biến động mạnh (2-3 vụ/năm), pattern theo mùa vụ rõ ràng
-  - **Cây lâu năm:** NDVI ổn định, thay đổi ít theo mùa
-  - **Đô thị:** NDVI thấp (~0.1-0.3), gần như không đổi
-  - **Rừng:** NDVI cao (~0.6-0.8), ổn định quanh năm
-- Simple seasonal averaging không phản ánh được đặc điểm riêng của từng loại đất
-
----
-
-## 2. Phương Pháp Đề Xuất: Land-Type-Specific Forecasting
-
-### 2.1 Tổng Quan Phương Pháp
-
-**Ý tưởng cốt lõi:** Mỗi loại đất có seasonal pattern khác nhau → Cần forecast riêng cho từng loại đất
-
-```
-Historical Data → Classify Land Types → Calculate Land-Type-Specific Patterns → Forecast
-```
-
-### 2.2 Quy Trình Chi Tiết
-
-#### **Bước 1: Thu Thập Dữ Liệu Lịch Sử**
-
-**Input:**
-- Bbox (min_lon, min_lat, max_lon, max_lat)
-- Historical lookback period (mặc định: 12 tháng)
-- Forecast period (start_date, end_date)
-
-**Process:**
-```python
-historical_end = forecast_start - 1 day
-historical_start = historical_end - N months
-```
-
-**Data source:** Microsoft Planetary Computer - Sentinel-2 L2A
-- Bands: B02, B03, B04, B05, B08, B11, SCL
-- Resolution: 10m, 20m, or 60m
-- Cloud masking: SCL != [0, 1, 3, 8, 9, 10]
-
-**Output:** Time series satellite data (n_timesteps × width × height × bands)
-
----
-
-#### **Bước 2: Tính Spectral Indices**
-
-**Công thức:**
-
-1. **NDVI** (Normalized Difference Vegetation Index)
-   ```
-   NDVI = (NIR - Red) / (NIR + Red)
-   NDVI = (B08 - B04) / (B08 + B04)
-   ```
-
-2. **NDWI** (Normalized Difference Water Index)
-   ```
-   NDWI = (Green - NIR) / (Green + NIR)
-   NDWI = (B03 - B08) / (B03 + B08)
-   ```
-
-3. **NDBI** (Normalized Difference Built-up Index)
-   ```
-   NDBI = (SWIR - NIR) / (SWIR + NIR)
-   NDBI = (B11 - B08) / (B11 + B08)
-   ```
-
-4. **EVI** (Enhanced Vegetation Index)
-   ```
-   EVI = 2.5 × (NIR - Red) / (NIR + 6×Red - 7.5×Blue + 1)
-   EVI = 2.5 × (B08 - B04) / (B08 + 6×B04 - 7.5×B02 + 1)
-   ```
-
-**Output:** 4 spectral indices × n_timesteps × width × height
-
----
-
-#### **Bước 3: Land Classification (Machine Learning)**
-
-**Purpose:** Phân loại từng pixel/point thành các loại đất
-
-**Process:**
-
-1. **Feature Extraction**
-   - Sample N random points (mặc định: 1000) trong bbox
-   - Tại mỗi point, extract aggregate features từ toàn bộ time series:
-     ```
-     features = [
-         ndvi_mean,   # Trung bình NDVI qua thời gian
-         ndvi_min,    # NDVI thấp nhất
-         ndvi_max,    # NDVI cao nhất
-         ndvi_std,    # Độ lệch chuẩn NDVI (phản ánh biến động)
-         ndvi_range,  # max - min
-         ndwi_mean,   # Trung bình NDWI
-         ndbi_mean,   # Trung bình NDBI
-         evi_mean     # Trung bình EVI
-     ]
-     ```
-
-2. **Classification**
-   - Load pre-trained model (XGBoost, RandomForest, CNN, etc.)
-   - Predict land type for each point:
-     ```python
-     land_types = model.predict(features)
-     ```
-
-3. **Land Type Distribution**
-   ```
-   Example output:
-   - Type 0 (Lúa nước): 450 points (45%)
-   - Type 1 (Cây lâu năm): 300 points (30%)
-   - Type 2 (Đô thị): 150 points (15%)
-   - Type 3 (Rừng): 100 points (10%)
-   ```
-
-**Advantage của approach này:**
-- Model đã được train để nhận diện pattern của từng loại đất
-- Features aggregate phản ánh đầy đủ temporal behavior
-- Classification accuracy ~80-90% (dựa vào model quality)
-
----
-
-#### **Bước 4: Calculate Land-Type-Specific Seasonal Patterns**
-
-**Purpose:** Tính seasonal pattern riêng cho từng loại đất
-
-**Process:**
-
-1. **Group by Land Type & Month**
-   ```python
-   for each timestep in historical_data:
-       month = timestep.month  # 1-12
-       
-       for each classified_point:
-           land_type = point.classification
-           ndvi_value = extract_ndvi_at(point, timestep)
-           
-           land_type_patterns[land_type][month].append({
-               'ndvi': ndvi_value,
-               'ndwi': ndwi_value,
-               'ndbi': ndbi_value,
-               'evi': evi_value
-           })
-   ```
-
-2. **Calculate Statistics per Land Type per Month**
-   ```python
-   for land_type in unique_land_types:
-       for month in 1..12:
-           values = land_type_patterns[land_type][month]
-           
-           seasonal_stats[land_type][month] = {
-               'ndvi_mean': mean(values.ndvi),
-               'ndvi_min': min(values.ndvi),
-               'ndvi_max': max(values.ndvi),
-               'ndvi_std': std(values.ndvi),
-               'ndvi_range': max - min,
-               'ndwi_mean': mean(values.ndwi),
-               'ndbi_mean': mean(values.ndbi),
-               'evi_mean': mean(values.evi),
-               'n_samples': len(values)
-           }
-   ```
-
-**Example Output:**
-```
-Land Type 0 (Lúa) - Month 1 (Tháng 1):
-  ndvi_mean: 0.45, ndvi_std: 0.12, n_samples: 120
-  
-Land Type 0 (Lúa) - Month 6 (Tháng 6):
-  ndvi_mean: 0.75, ndvi_std: 0.08, n_samples: 135
-  
-Land Type 3 (Rừng) - Month 1:
-  ndvi_mean: 0.78, ndvi_std: 0.03, n_samples: 45
-  
-Land Type 3 (Rừng) - Month 6:
-  ndvi_mean: 0.81, ndvi_std: 0.02, n_samples: 48
-```
-
-**Insight:**
-- Lúa: NDVI thay đổi rất lớn (0.45 → 0.75)
-- Rừng: NDVI ổn định (0.78 → 0.81)
-- Std của lúa cao hơn rừng (biến động nhiều hơn)
-
----
-
-#### **Bước 5: Forecast Using Weighted Average**
-
-**Purpose:** Dự đoán NDVI tương lai bằng cách kết hợp patterns của tất cả land types
-
-**Process:**
-
-1. **Calculate Land Type Weights**
-   ```python
-   weights = {
-       land_type: count(land_type) / total_points
-   }
-   
-   Example:
-   weights = {
-       0: 0.45,  # 45% lúa
-       1: 0.30,  # 30% cây lâu năm
-       2: 0.15,  # 15% đô thị
-       3: 0.10   # 10% rừng
-   }
-   ```
-
-2. **Generate Forecast for Each Month**
-   ```python
-   for forecast_month in forecast_period:
-       month_number = forecast_month.month  # 1-12
-       
-       # Weighted average across all land types
-       forecast = {
-           'ndvi_mean': 0,
-           'ndvi_min': 0,
-           'ndvi_max': 0,
-           ...
-       }
-       
-       for land_type, weight in weights.items():
-           pattern = seasonal_stats[land_type][month_number]
-           
-           forecast['ndvi_mean'] += pattern['ndvi_mean'] * weight
-           forecast['ndvi_min'] += pattern['ndvi_min'] * weight
-           forecast['ndvi_max'] += pattern['ndvi_max'] * weight
-           ...
-       
-       timeseries.append({
-           'date': forecast_month,
-           **forecast,
-           'land_type_contributions': {
-               land_type: {
-                   **seasonal_stats[land_type][month_number],
-                   'weight': weight
-               }
-           }
-       })
-   ```
-
-**Example Calculation:**
-```
-Forecast for June 2026:
-
-Type 0 (Lúa, 45%):    NDVI = 0.75
-Type 1 (Cây, 30%):    NDVI = 0.65
-Type 2 (Đô thị, 15%): NDVI = 0.25
-Type 3 (Rừng, 10%):   NDVI = 0.81
-
-Weighted NDVI = 0.75×0.45 + 0.65×0.30 + 0.25×0.15 + 0.81×0.10
-              = 0.3375 + 0.195 + 0.0375 + 0.081
-              = 0.651
-```
-
-**Output Format:**
-```json
-{
-  "timeseries": [
-    {
-      "date": "2026-06-01",
-      "ndvi_mean": 0.651,
-      "ndvi_min": 0.42,
-      "ndvi_max": 0.83,
-      "ndvi_std": 0.15,
-      "ndvi_range": 0.41,
-      "ndwi_mean": -0.22,
-      "ndbi_mean": -0.15,
-      "evi_mean": 0.48,
-      "is_forecast": true,
-      "land_type_specific": {
-        "0": {"ndvi_mean": 0.75, "weight": 0.45, ...},
-        "1": {"ndvi_mean": 0.65, "weight": 0.30, ...},
-        "2": {"ndvi_mean": 0.25, "weight": 0.15, ...},
-        "3": {"ndvi_mean": 0.81, "weight": 0.10, ...}
-      }
-    },
-    ...
-  ],
-  "method": "Land-Type-Specific Forecasting",
-  "land_types_detected": [0, 1, 2, 3]
-}
-```
-
----
-
-## 3. So Sánh Phương Pháp
-
-### 3.1 Simple Seasonal Averaging (Baseline)
-
-**Quy trình:**
-1. Tính NDVI trung bình cho từng tháng trong historical period
-2. Áp dụng trực tiếp cho tương lai
-
-**Ưu điểm:**
-- Đơn giản, nhanh
-- Không cần model ML
-
-**Nhược điểm:**
-- Không phân biệt loại đất
-- Lúa và rừng được average chung → Kết quả không phản ánh đúng
-- Accuracy: ~60-70%
-
-**Example:**
-```
-Historical average for June (all land types mixed):
-  NDVI_mean = 0.55
-
-→ Forecast for June 2026: NDVI = 0.55 (cho tất cả vùng)
-```
-
-**Vấn đề:** Vùng lúa thực tế có NDVI = 0.75 vào tháng 6, nhưng forecast chỉ ra 0.55
-
----
-
-### 3.2 Land-Type-Specific Forecasting (Đề xuất)
-
-**Quy trình:**
-1. Classify đất bằng ML → Biết 45% lúa, 30% cây, 15% đô thị, 10% rừng
-2. Tính pattern riêng: Lúa tháng 6 = 0.75, Rừng tháng 6 = 0.81
-3. Weighted average theo tỉ lệ land types
-
-**Ưu điểm:**
-- Phản ánh đúng đặc điểm từng loại đất
-- Tận dụng model classification đã train
-- Accuracy: ~75-85% (+15-25% so với baseline)
-
-**Nhược điểm:**
-- Cần model ML (phức tạp hơn)
-- Tính toán lâu hơn (~20-30s thay vì ~10s)
-
-**Example:**
-```
-Forecast for June 2026:
-  45% Lúa (0.75) + 30% Cây (0.65) + 15% Đô thị (0.25) + 10% Rừng (0.81)
-  = 0.651
-
-→ Chính xác hơn nhiều so với simple average 0.55
-```
-
----
-
-## 4. Độ Chính Xác & Đánh Giá
-
-### 4.1 Metrics
-
-**Accuracy Improvement:**
-- **Simple Seasonal:** 60-70% correlation với actual values
-- **Land-Type-Specific:** 75-85% correlation (+15-25% improvement)
-
-**Mean Absolute Error (MAE):**
-- **Simple Seasonal:** MAE ~0.08-0.12 NDVI units
-- **Land-Type-Specific:** MAE ~0.04-0.07 NDVI units (giảm 40-50%)
-
-### 4.2 Khi Nào Method Hoạt Động Tốt?
-
-**Điều kiện thuận lợi:**
-✅ Khu vực có nhiều loại đất khác nhau (mixed land use)
-✅ Seasonal pattern rõ ràng (mùa khô/mưa phân biệt)
-✅ Historical data đủ dài (≥12 tháng)
-✅ Model classification có accuracy cao (>80%)
-
-**Điều kiện khó khăn:**
-⚠️ Khu vực đồng nhất (toàn lúa hoặc toàn rừng) → Ít lợi thế so với simple
-⚠️ Climate change/extreme events → Pattern không lặp lại
-⚠️ Land use thay đổi (construction, deforestation) → Historical pattern không còn phù hợp
-
-### 4.3 Validation Approach
-
-**Backtesting:**
-1. Dùng data 2023 để forecast tháng 6/2024
-2. So sánh forecast vs actual satellite data tháng 6/2024
-3. Calculate metrics: Correlation, MAE, RMSE
-
-**Cross-validation:**
-- Split historical data thành train/test
-- Train pattern trên 10 tháng, test trên 2 tháng
-- Repeat 6 lần (rolling window)
-
----
-
-## 5. Ứng Dụng Thực Tế
-
-### 5.1 Use Cases
-
-**1. Nông nghiệp - Crop Forecasting**
-- Dự đoán NDVI lúa 2-3 tháng trước
-- Ước tính năng suất dựa trên NDVI forecast
-- Planning irrigation, fertilizer
-
-**2. Climate Monitoring**
-- Dự đoán drought risk (NDVI giảm bất thường)
-- Track vegetation health trends
-- Early warning system
-
-**3. Urban Planning**
-- Forecast green space changes
-- Monitor urban expansion impact
-- Environmental impact assessment
-
-**4. Forest Management**
-- Predict forest health
-- Deforestation early detection
-- Reforestation monitoring
-
-### 5.2 Hạn Chế & Lưu Ý
-
-**⚠️ Limitations:**
-
-1. **Không phải Deep Learning Forecasting**
-   - Method này là statistical pattern matching, không phải LSTM/GRU time series prediction
-   - Không học được trends, anomalies phức tạp
-   - Giả định pattern lặp lại (stationary assumption)
-
-2. **Sensitivity to Historical Period**
-   - Nếu historical period có anomaly (drought, flood) → Forecast bị sai
-   - Cần chọn representative historical period
-
-3. **Model Quality Dependency**
-   - Nếu land classification sai (accuracy <70%) → Forecast kém
-   - Cần retrain model khi land use thay đổi
-
-4. **Spatial Resolution Limitation**
-   - Forecast theo weighted average → Mất không gian chi tiết
-   - Không predict được pixel-level NDVI map
-
-**💡 Recommendations:**
-
-- ✅ Dùng cho short-term forecast (1-3 tháng)
-- ✅ Combine với other data sources (weather forecast, soil moisture)
-- ✅ Regular model retraining (mỗi 6-12 tháng)
-- ✅ Validate bằng actual data khi có
-- ⚠️ Không dùng cho long-term forecast (>6 tháng)
-- ⚠️ Cẩn thận với climate change impacts
-
----
-
-## 6. Implementation Details
-
-### 6.1 API Endpoint
-
-**Endpoint:** `POST /api/ndvi/forecast`
-
-**Request Body:**
-```json
-{
-  "bbox": [105.8, 9.4, 106.0, 9.6],
-  "forecast_start_date": "2026-06-01",
-  "forecast_end_date": "2026-12-31",
-  "historical_months": 12,
-  "model_filename": "model_odc.joblib",
-  "sample_points": 1000,
-  "resolution": 20,
-  "max_cloud_cover": 30,
-  "max_scenes": 20
-}
-```
-
-**Parameters:**
-- `bbox`: [min_lon, min_lat, max_lon, max_lat]
-- `forecast_start_date`: Bắt đầu forecast (có thể là tương lai)
-- `forecast_end_date`: Kết thúc forecast
-- `historical_months`: Số tháng lịch sử để tính pattern (mặc định: 12)
-- `model_filename`: Tên file model để classify (optional, nếu null → simple seasonal)
-- `sample_points`: Số điểm để sample cho classification (mặc định: 1000)
-- `resolution`: Độ phân giải (10/20/60m)
-- `max_cloud_cover`: Cloud cover tối đa (%)
-- `max_scenes`: Số scenes tối đa
-
-**Response:**
-```json
-{
-  "timeseries": [
-    {
-      "date": "2026-06-01",
-      "ndvi_mean": 0.651,
-      "ndvi_min": 0.42,
-      "ndvi_max": 0.83,
-      "ndvi_std": 0.15,
-      "ndvi_range": 0.41,
-      "ndwi_mean": -0.22,
-      "ndbi_mean": -0.15,
-      "evi_mean": 0.48,
-      "is_forecast": true,
-      "land_type_specific": {
-        "0": {"ndvi_mean": 0.75, "weight": 0.45},
-        "1": {"ndvi_mean": 0.65, "weight": 0.30},
-        "2": {"ndvi_mean": 0.25, "weight": 0.15},
-        "3": {"ndvi_mean": 0.81, "weight": 0.10}
-      }
-    }
-  ],
-  "n_forecast_points": 7,
-  "mean_ndvi": 0.642,
-  "min_ndvi": 0.38,
-  "max_ndvi": 0.85,
-  "method": "Land-Type-Specific Forecasting (ML-Enhanced)",
-  "model_used": "model_odc.joblib",
-  "land_types_detected": [0, 1, 2, 3],
-  "forecast_period": "2026-06-01 to 2026-12-31",
-  "historical_period": "2025-06-01 to 2026-05-31"
-}
-```
-
-### 6.2 Frontend Integration
-
-**Mode Selection:**
-```javascript
-// Two modes:
-1. Historical Analysis: Dùng ML model analyze historical satellite data
-2. Forecast Mode: Predict future NDVI using land-type-specific patterns
-```
-
-**User Flow:**
-1. Chọn "🔮 Dự đoán tương lai"
-2. Chọn bbox (hoặc chọn tỉnh)
-3. Chọn forecast period (VD: 2026-06-01 → 2026-12-31)
-4. Chọn model (optional) → Nếu không chọn = simple seasonal
-5. Click "🔮 Dự đoán NDVI Tương Lai"
-6. Xem kết quả: Chart + table + download CSV/PNG
-
----
-
-## 7. Future Improvements
-
-### 7.1 Short-term Enhancements
-
-**1. Multi-Model Ensemble**
-- Combine predictions từ multiple models
-- Voting/averaging để tăng stability
-- Estimated improvement: +5-10% accuracy
-
-**2. Confidence Intervals**
-- Calculate uncertainty bounds
-- Show prediction range: NDVI_mean ± confidence
-- Help users understand forecast reliability
-
-**3. Weather Integration**
-- Integrate weather forecast data (rainfall, temperature)
-- Adjust seasonal patterns based on predicted weather
-- Especially useful for drought/flood predictions
-
-### 7.2 Long-term Research Directions
-
-**1. Deep Learning Time Series Models**
-- LSTM/GRU for true time series forecasting
-- Learn temporal dependencies beyond seasonal patterns
-- Potential accuracy: 85-95%
-
-**2. Hybrid Physics-ML Model**
-- Combine crop growth models (DSSAT, WOFOST) với ML
-- Physics-based constraints + data-driven learning
-- More robust to climate change
-
-**3. Transfer Learning**
-- Pre-train on global satellite data
-- Fine-tune on local regions
-- Better generalization
-
-**4. Spatial-Temporal Models**
-- CNN-LSTM cho pixel-level forecasting
-- Preserve spatial structure
-- Generate full NDVI maps (not just averaged values)
-
----
-
-## 8. Kết Luận
-
-### 8.1 Tóm Tắt
-
-**Method:** Land-Type-Specific Seasonal Forecasting
-
-**Core Innovation:** 
-Thay vì tính seasonal average chung cho toàn khu vực, ta:
-1. Dùng ML phân loại đất
-2. Tính pattern riêng cho từng loại
-3. Kết hợp theo tỉ lệ diện tích
-
-**Key Results:**
-- ✅ Accuracy: 75-85% (vs 60-70% baseline)
-- ✅ MAE giảm 40-50%
-- ✅ Tận dụng model classification đã train
-- ✅ Không cần train thêm model mới
-- ⚠️ Chỉ phù hợp cho short-term (1-6 tháng)
-
-### 8.2 Ý Nghĩa Khoa Học
-
-**Contributions:**
-1. Kết hợp supervised learning (classification) với time series forecasting
-2. Demonstrate tầm quan trọng của land-type heterogeneity
-3. Practical approach có thể áp dụng ngay với existing models
-
-**Applications:**
-- Agriculture: Crop yield prediction
-- Environmental monitoring: Drought early warning
-- Urban planning: Green space management
-- Climate research: Vegetation response to climate
-
-### 8.3 Đề Xuất Tiếp Theo
-
-**For Production:**
-1. ✅ Implement API endpoint (DONE)
-2. ✅ Frontend integration (DONE)
-3. 🔄 Validate with real data (TODO)
-4. 🔄 Monitor accuracy over time (TODO)
-5. 🔄 Setup automated retraining pipeline (TODO)
-
-**For Research:**
-1. Compare với LSTM/GRU time series models
-2. Test different classification algorithms
-3. Experiment với ensemble methods
-4. Publish results in remote sensing journals
-
----
-
-## 9. References & Resources
-
-### 9.1 Data Sources
-- **Microsoft Planetary Computer:** https://planetarycomputer.microsoft.com/
-- **Sentinel-2 L2A:** ESA Copernicus Program
-- **STAC API:** https://stacspec.org/
-
-### 9.2 Libraries Used
-```python
-# Satellite data access
-pystac-client==0.7.5
-planetary-computer==1.0.0
-odc-stac==0.3.8
-
-# Machine Learning
-scikit-learn==1.3.2
-xgboost==2.0.2
-
-# Data processing
-numpy==1.24.3
-pandas==2.0.3
-xarray==2023.7.0
-
-# Geospatial
-rasterio==1.3.9
-```
-
-### 9.3 Related Papers
-1. Weiss, M. et al. (2020). "Remote sensing for agricultural applications: A meta-review"
-2. Zhang, X. et al. (2021). "Deep learning for vegetation mapping using time series satellite data"
-3. Nguyen, D. et al. (2023). "Land classification in Vietnam using Sentinel-2 data"
-
-### 9.4 Model Training Notebooks
-- `01.train_ODC.ipynb`: Original training methodology
-- `01.train_ODC_XGBoost.ipynb`: XGBoost implementation
-- `feature_extractor.py`: Feature extraction module
-
----
-
-## 10. Phụ Lục (Appendix)
-
-### 10.1 Spectral Index Formulas
-
-| Index | Formula | Range | Interpretation |
-|-------|---------|-------|----------------|
-| NDVI | (NIR - Red) / (NIR + Red) | [-1, 1] | Vegetation health: <0.2 (bare), 0.2-0.5 (sparse), >0.6 (dense) |
-| NDWI | (Green - NIR) / (Green + NIR) | [-1, 1] | Water content: >0.3 (water), -0.1 to 0.3 (vegetation), <-0.1 (dry) |
-| NDBI | (SWIR - NIR) / (SWIR + NIR) | [-1, 1] | Built-up: >0 (urban), <0 (vegetation) |
-| EVI | 2.5 × (NIR - Red) / (NIR + 6×Red - 7.5×Blue + 1) | [-1, 1] | Enhanced vegetation (less saturation than NDVI) |
-
-### 10.2 Land Classification Types (Example)
-
-| Type ID | Land Use | Typical NDVI | Typical Pattern |
-|---------|----------|--------------|-----------------|
-| 0 | Lúa nước (Paddy rice) | 0.3 - 0.8 | High variance, 2-3 peaks/year |
-| 1 | Cây lâu năm (Perennial crops) | 0.5 - 0.7 | Stable, low variance |
-| 2 | Đô thị (Urban) | 0.1 - 0.3 | Very low, constant |
-| 3 | Rừng (Forest) | 0.6 - 0.8 | High, stable |
-| 4 | Đất trống (Barren) | 0.0 - 0.2 | Very low |
-| 5 | Nước (Water) | -0.3 - 0.1 | Negative or low |
-
-### 10.3 Sample API Call (cURL)
-
-```bash
-curl -X POST "http://localhost:8000/api/ndvi/forecast" \
-  -H "Content-Type: application/json" \
-  -d '{
-    "bbox": [105.8, 9.4, 106.0, 9.6],
-    "forecast_start_date": "2026-06-01",
-    "forecast_end_date": "2026-12-31",
-    "historical_months": 12,
-    "model_filename": "model_odc.joblib",
-    "sample_points": 1000,
-    "resolution": 20,
-    "max_cloud_cover": 30
-  }'
-```
-
-### 10.4 Glossary
-
-- **NDVI:** Normalized Difference Vegetation Index - Chỉ số thực vật chuẩn hóa
-- **Sentinel-2:** European satellite constellation for Earth observation
-- **Bbox:** Bounding box - Khung giới hạn địa lý (min_lon, min_lat, max_lon, max_lat)
-- **Time series:** Chuỗi thời gian - Dữ liệu theo thời gian
-- **Seasonal pattern:** Mẫu theo mùa - Pattern lặp lại theo chu kỳ năm
-- **Land classification:** Phân loại đất - Xác định loại sử dụng đất
-- **Spectral index:** Chỉ số quang phổ - Công thức kết hợp các band vệ tinh
-- **Cloud masking:** Lọc mây - Loại bỏ pixels bị che phủ bởi mây
-
----
-
-**Document Version:** 1.0  
-**Last Updated:** January 4, 2026  
-**Contact:** Remote Sensing Analysis System  
-**License:** Internal Use Only
-
----
-
-## Citation
-
-Nếu sử dụng methodology này trong báo cáo/paper, cite như sau:
-
-```
-Remote Sensing Analysis System (2026). 
-"NDVI Time Series Forecasting using Land-Type-Specific Seasonal Patterns." 
-Internal Technical Report, Version 1.0.
-```

NDVI_PREDICTION_GUIDE.md

@@ -1,202 +0,0 @@
-# Hướng Dẫn Sử Dụng Chức Năng Predict NDVI
-
-## Tổng Quan
-Chức năng mới cho phép dự đoán phân loại đất (land classification) **kết hợp** với việc xuất ra raster NDVI cho cùng một khu vực.
-
-## Cách Sử Dụng
-
-### 1. Truy cập Prediction Interface
-- Mở trình duyệt: `http://localhost:8000/prediction`
-- Hoặc từ trang chủ, click vào **Prediction**
-
-### 2. Chọn Model
-- Chọn model đã được train từ dropdown "Select Model"
-- Model phải tồn tại trong thư mục `model_train/`
-
-### 3. Vẽ Khu Vực (Bbox)
-- Sử dụng công cụ vẽ hình chữ nhật trên bản đồ
-- Khu vực này sẽ được dùng để:
-  - Load dữ liệu vệ tinh
-  - Tính NDVI
-  - Predict land classification
-
-### 4. Cấu Hình Thời Gian & Dữ Liệu
-- **Từ ngày / Đến ngày**: Khoảng thời gian lấy ảnh vệ tinh
-- **Max Scenes**: Số lượng ảnh tối đa (khuyến nghị: 12)
-- **Cloud Cover**: % mây tối đa (khuyến nghị: 30%)
-- **Resolution**: Độ phân giải (10m hoặc 20m)
-
-### 5. Bật Export NDVI
-- ✅ Check vào "🌿 Export NDVI Raster"
-- Khi bật, hệ thống sẽ:
-  - Tính NDVI từ Sentinel-2 (NIR - Red) / (NIR + Red)
-  - Xuất ra file `ndvi_YYYYMMDD_HHMMSS.tif`
-  - Xuất ra file `classification_YYYYMMDD_HHMMSS.tif`
-
-### 6. Chạy Prediction
-- Click "🚀 Start Prediction (với NDVI)"
-- Hệ thống sẽ:
-  1. Load dữ liệu Sentinel-2 (bands: B02, B03, B04, B08)
-  2. Tính toán các spectral indices (NDVI, NDWI, NDBI)
-  3. Dùng model để predict land classification
-  4. Xuất kết quả
-
-## Kết Quả
-
-### Output Files
-Sau khi hoàn thành, bạn sẽ nhận được 2 file trong thư mục `predictions/`:
-
-1. **`ndvi_YYYYMMDD_HHMMSS.tif`**
-   - GeoTIFF chứa giá trị NDVI
-   - Giá trị: -1 đến +1
-   - CRS: EPSG:4326 (WGS84)
-   - Có thể mở bằng QGIS, ArcGIS, hoặc Python
-
-2. **`classification_YYYYMMDD_HHMMSS.tif`**
-   - GeoTIFF chứa kết quả phân loại đất
-   - Giá trị: class labels (ví dụ: 0, 1, 2, 3...)
-   - CRS: EPSG:4326 (WGS84)
-
-### Thống Kê Hiển Thị
-Sau khi predict xong, giao diện sẽ hiển thị:
-- **NDVI Statistics**:
-  - Mean: Giá trị NDVI trung bình
-  - Min: Giá trị NDVI nhỏ nhất
-  - Max: Giá trị NDVI lớn nhất
-  - Std: Độ lệch chuẩn
-- **Class Distribution**: Số lượng pixel cho mỗi class
-- **N Scenes**: Số ảnh vệ tinh đã sử dụng
-
-## API Endpoint
-
-### POST `/api/predict/with-ndvi`
-
-**Request Body:**
-```json
-{
-  "model_filename": "model_xgboost_20231221_120000.joblib",
-  "min_lon": 105.6,
-  "min_lat": 9.3,
-  "max_lon": 106.2,
-  "max_lat": 9.8,
-  "start_date": "2023-03-01",
-  "end_date": "2023-05-31",
-  "max_scenes": 12,
-  "cloud_cover": 30,
-  "resolution": 20,
-  "export_ndvi": true,
-  "export_classification": true
-}
-```
-
-**Response:**
-```json
-{
-  "success": true,
-  "message": "Prediction with NDVI completed",
-  "output_files": [
-    {"type": "ndvi", "path": "predictions/ndvi_20231221_120000.tif"},
-    {"type": "classification", "path": "predictions/classification_20231221_120000.tif"}
-  ],
-  "ndvi_stats": {
-    "mean": 0.456,
-    "min": -0.123,
-    "max": 0.789,
-    "std": 0.234
-  },
-  "class_distribution": {
-    "0": 12345,
-    "1": 23456,
-    "2": 34567
-  },
-  "n_scenes": 12,
-  "resolution": 20,
-  "bbox": [105.6, 9.3, 106.2, 9.8]
-}
-```
-
-## Download Files
-
-Sau khi prediction hoàn thành, có thể download files qua:
-- **UI**: Click "💾 Download GeoTIFF" trong kết quả
-- **API**: `GET /api/predictions/download/ndvi_YYYYMMDD_HHMMSS.tif`
-- **API**: `GET /api/predictions/download/classification_YYYYMMDD_HHMMSS.tif`
-
-## Sử Dụng Kết Quả với Python
-
-```python
-import rasterio
-import matplotlib.pyplot as plt
-import numpy as np
-
-# Read NDVI raster
-with rasterio.open('predictions/ndvi_20231221_120000.tif') as src:
-    ndvi = src.read(1)
-    
-    # Visualize
-    plt.figure(figsize=(10, 8))
-    plt.imshow(ndvi, cmap='RdYlGn', vmin=-1, vmax=1)
-    plt.colorbar(label='NDVI')
-    plt.title('NDVI Map')
-    plt.show()
-
-# Read classification raster
-with rasterio.open('predictions/classification_20231221_120000.tif') as src:
-    classification = src.read(1)
-    
-    # Visualize
-    plt.figure(figsize=(10, 8))
-    plt.imshow(classification, cmap='tab10')
-    plt.colorbar(label='Land Class')
-    plt.title('Land Classification')
-    plt.show()
-```
-
-## Sử Dụng Kết Quả với QGIS
-
-1. Mở QGIS
-2. **Layer → Add Layer → Add Raster Layer**
-3. Chọn file `ndvi_*.tif` hoặc `classification_*.tif`
-4. Styling:
-   - NDVI: Singleband pseudocolor, min=-1, max=1, color ramp=RdYlGn
-   - Classification: Paletted/Unique values
-
-## Lưu Ý
-
-- **Thời gian xử lý**: Tùy thuộc vào kích thước bbox và số scenes (thường 2-5 phút)
-- **Bộ nhớ**: Khu vực lớn + resolution cao = RAM cao
-- **NDVI values**:
-  - < 0: Nước, đất trống
-  - 0 - 0.2: Đất có ít thực vật
-  - 0.2 - 0.5: Cây cỏ, cây trồng
-  - > 0.5: Rừng rậm, thực vật dày đặc
-
-## So Sánh với NDVI Time Series
-
-| Feature | Predict NDVI | NDVI Time Series |
-|---------|-------------|------------------|
-| **Mục đích** | Xuất raster NDVI + land classification | Xem xu hướng NDVI theo thời gian |
-| **Output** | GeoTIFF files | Chart, CSV |
-| **Dùng model** | Có (predict land class) | Không (chỉ tính NDVI) |
-| **Visualize** | Bản đồ raster | Biểu đồ đường |
-| **Use case** | Phân tích không gian | Phân tích thời gian |
-
-## Troubleshooting
-
-**Q: Lỗi "Model không tồn tại"?**
-- Kiểm tra model đã được train và lưu trong `model_train/`
-- Refresh danh sách model
-
-**Q: Kết quả NDVI toàn NaN?**
-- Check cloud cover (giảm xuống)
-- Mở rộng time range
-- Kiểm tra bbox có nằm trong phạm vi Sentinel-2 coverage
-
-**Q: File GeoTIFF không mở được?**
-- Đảm bảo file download hoàn chỉnh
-- Dùng QGIS hoặc rasterio để kiểm tra
-
-**Q: Prediction chậm?**
-- Giảm resolution (20m thay vì 10m)
-- Giảm max_scenes
-- Thu nhỏ bbox

NDVI_VS_LAND_CLASSIFICATION.md

@@ -1,116 +0,0 @@
-# QUAN TRỌNG: Làm rõ về NDVI và Phân loại Đất
-
-## Mục tiêu chính: PHÂN LOẠI SỬ DỤNG ĐẤT
-
-Hệ thống phân loại 8 loại đất:
-1. **Lua tom** (0): Lúa tôm
-2. **Lua** (1): Lúa
-3. **CHN** (2): Cây hàng năm
-4. **CLN** (3): Cây lâu năm  
-5. **TS** (4): Thủy sản
-6. **Song** (5): Sông
-7. **Dat xay dung** (6): Đất xây dựng
-8. **Rung** (7): Rừng
-
-## Workflow Đúng
-
-### Training:
-```
-Sentinel-2 Data (nhiều bands) 
-  → Extract Features (spectral bands, indices, temporal) 
-  → Train Model (RandomForest/XGBoost/CNN)
-  → Model dự đoán loại đất (0-7)
-```
-
-### Prediction:
-```
-Sentinel-2 Data (khu vực mới)
-  → Extract Features (giống training)
-  → Model.predict() 
-  → Kết quả: Bản đồ phân loại đất (0-7)
-  → [OPTIONAL] Tính NDVI để visualization/analysis
-```
-
-## NDVI là gì?
-
-**NDVI (Normalized Difference Vegetation Index)** là chỉ số thực vật:
-- Formula: `NDVI = (NIR - Red) / (NIR + Red)`
-- Giá trị: -1 đến +1
-- Ý nghĩa: 
-  - Cao (>0.6): Thực vật xanh tươi (rừng, lúa)
-  - Trung (0.2-0.6): Thực vật thưa, cỏ
-  - Thấp (<0.2): Đất trống, nước, xây dựng
-
-## Vai trò của NDVI
-
-### ❌ KHÔNG PHẢI: Input duy nhất cho model
-```python
-# SAI - Chỉ dùng NDVI để predict loại đất
-X = [ndvi_value]  # 1 feature
-model.predict(X)  # Accuracy thấp!
-```
-
-### ✅ ĐÚNG: Một trong nhiều features
-```python
-# ĐÚNG - Dùng nhiều features
-X = [ndvi, ndwi, ndbi, blue, green, red, nir, swir1, swir2, ...]  # 39 features
-model.predict(X)  # Accuracy cao!
-```
-
-### ✅ ĐÚNG: Chỉ số phụ sau prediction
-```python
-# 1. Predict land use
-predictions = model.predict(features)  # → [0,1,2,3,4,5,6,7]
-
-# 2. Calculate NDVI for visualization
-ndvi = (nir - red) / (nir + red)
-
-# 3. Export both
-save_geotiff("land_classification.tif", predictions)
-save_geotiff("ndvi.tif", ndvi)  # Chỉ số phụ để xem thêm
-```
-
-## Model hiện tại: model_odc.joblib
-
-```json
-{
-  "n_features": 39,
-  "model_type": "random_forest (GridSearchCV)",
-  "purpose": "Phân loại sử dụng đất (8 classes)",
-  "features": [
-    "Spectral bands từ nhiều time steps",
-    "Spectral indices (NDVI, NDWI, NDBI, EVI, ...)",
-    "Temporal features (min, max, mean, std, range)"
-  ]
-}
-```
-
-## So sánh với Notebook 01.train_ODC.ipynb
-
-Notebook này train model **ĐƠN GIẢN HÓA** chỉ để demo:
-- Chỉ dùng 1 feature (NDVI)
-- Accuracy thấp
-- **KHÔNG phải** model production
-
-Model thực tế (model_odc.joblib):
-- Dùng 39 features
-- Accuracy cao hơn
-- Production-ready
-
-## Kết luận
-
-✅ **Prediction workflow**:
-1. Load Sentinel-2 data
-2. Extract 39 features (bands + indices + temporal)
-3. Model.predict() → Land classification map
-4. [Optional] Calculate NDVI for additional analysis
-
-✅ **NDVI role**:
-- Là MỘT trong các features (không phải duy nhất)
-- Hoặc là output phụ để visualization
-- KHÔNG phải mục tiêu chính
-
-❌ **Sai lầm thường gặp**:
-- Nghĩ NDVI là input duy nhất
-- Train model chỉ với NDVI → accuracy thấp
-- Bỏ qua các features khác (NDWI, NDBI, temporal, ...)

NEW_FEATURES.md

@@ -1,252 +0,0 @@
-# 🎉 Chức năng mới đã được phục hồi
-
-## 📊 1. Dashboard Tổng Quan & Visualization
-
-Dashboard cung cấp giao diện trực quan để theo dõi hiệu suất hệ thống.
-
-### Truy cập Dashboard
-```
-http://localhost:8000/dashboard
-```
-
-### Tính năng
-- **📈 Tổng Quan**: Hiển thị thống kê tổng hợp
-  - Số models đã train
-  - Số predictions đã tạo
-  - Số reports đã generate
-  - Accuracy của model mới nhất
-
-- **📊 Accuracy Trends**: Biểu đồ theo dõi accuracy qua thời gian
-  - Line chart: Accuracy, Precision, Recall
-  - Bar chart: F1-Score comparison
-  - Export PNG/PDF
-
-- **📊 Class Distribution**: Phân bố các lớp đất
-  - Bar chart: Số lượng mẫu mỗi lớp
-  - Chọn model để xem
-  - Export PNG/PDF
-
-### API Endpoints
-
-```python
-# Lấy accuracy trends
-GET /api/dashboard/accuracy-trends
-
-# Lấy thống kê tổng quan
-GET /api/dashboard/statistics
-
-# Lấy phân bố lớp của model
-GET /api/dashboard/class-distribution/{model_filename}
-```
-
-### Export Charts
-- **PNG**: Click nút "💾 Export PNG"
-- **PDF**: Click nút "📄 Export PDF"
-
----
-
-## 📝 2. Auto Report Generator
-
-Report tự động được tạo sau khi training và prediction hoàn thành.
-
-### Reports đã có
-- **Training Report**: Tự động tạo sau khi train xong
-  - Metrics, confusion matrix, class distribution
-  - Lưu trong folder `reports/`
-  
-- **Prediction Report**: Tự động tạo sau khi predict xong
-  - Thông tin về output file, bbox, features
-  - Lưu trong folder `reports/`
-
-### API Endpoints
-
-```python
-# Liệt kê reports
-GET /api/reports/list
-
-# Xem report
-GET /api/reports/view/{filename}
-
-# Download report
-GET /api/reports/download/{filename}
-```
-
-### Xem Reports
-- Web interface: http://localhost:8000/
-- Hoặc truy cập trực tiếp: http://localhost:8000/api/reports/view/{filename}
-
----
-
-## 🔄 3. Batch Processing
-
-Predict nhiều khu vực cùng lúc với queue management.
-
-### Cách sử dụng
-
-#### Bước 1: Tạo CSV file
-Tạo file CSV với format:
-```csv
-name,min_lon,min_lat,max_lon,max_lat,start_date,end_date,max_scenes,cloud_cover,resolution
-Region_1,105.6,9.3,105.8,9.5,2023-03-01,2023-05-31,12,30,20
-Region_2,105.8,9.3,106.0,9.5,2023-03-01,2023-05-31,12,30,20
-```
-
-**File mẫu**: `batch_regions_example.csv`
-
-#### Bước 2: Upload và Start Batch
-1. Truy cập: http://localhost:8000/dashboard
-2. Chọn tab "🔄 Batch Processing"
-3. Upload CSV file
-4. Chọn model để predict
-5. Click "🚀 Start Batch Prediction"
-
-#### Bước 3: Theo dõi Progress
-Dashboard sẽ tự động refresh mỗi 3 giây và hiển thị:
-- ⏳ Queued: Đang chờ
-- ▶️ Running: Đang chạy
-- ✅ Completed: Hoàn thành
-- ❌ Failed: Lỗi
-
-### API Endpoints
-
-```python
-# Bắt đầu batch prediction
-POST /api/batch/start
-{
-    "model_filename": "model_20231221.joblib",
-    "items": [
-        {
-            "name": "Region_1",
-            "min_lon": 105.6,
-            "min_lat": 9.3,
-            "max_lon": 105.8,
-            "max_lat": 9.5,
-            "start_date": "2023-03-01",
-            "end_date": "2023-05-31",
-            "max_scenes": 12,
-            "cloud_cover": 30,
-            "resolution": 20
-        }
-    ],
-    "auto_retry": true,
-    "max_retries": 3
-}
-
-# Kiểm tra queue status
-GET /api/batch/status
-
-# Lấy kết quả batch
-GET /api/batch/results/{batch_id}
-
-# Hủy batch
-POST /api/batch/cancel/{batch_id}
-```
-
-### Auto-Retry
-- Tự động retry khi failed (default: max 3 lần)
-- Có thể tắt bằng cách set `auto_retry: false`
-
-### Progress Tracking
-- Mỗi job có progress bar riêng
-- Real-time update status
-- Hiển thị error message nếu failed
-
----
-
-## 🚀 Khởi động Server
-
-```bash
-# Activate môi trường
-conda activate env_01
-
-# Chạy API server
-python api_server.py
-```
-
-Server sẽ chạy tại: http://localhost:8000
-
-## 📍 Các URL quan trọng
-
-- **Training Interface**: http://localhost:8000/
-- **Dashboard**: http://localhost:8000/dashboard
-- **API Docs**: http://localhost:8000/docs
-- **Redoc**: http://localhost:8000/redoc
-
----
-
-## 🔧 Cấu trúc Folders
-
-```
-remote-sensing/
-├── api_server.py              # API server với các chức năng mới
-├── dashboard.html             # Dashboard UI (MỚI)
-├── training_interface.html    # Training UI
-├── report_generator.py        # Auto report generator
-├── batch_regions_example.csv  # CSV mẫu cho batch (MỚI)
-├── model_train/               # Models đã train
-├── predictions/               # Prediction outputs
-└── reports/                   # Auto-generated reports
-```
-
----
-
-## 🎯 Use Cases
-
-### Use Case 1: Theo dõi Model Performance
-1. Train nhiều models với configs khác nhau
-2. Mở Dashboard → Tab "📊 Accuracy Trends"
-3. So sánh accuracy/F1-score qua thời gian
-4. Export charts để báo cáo
-
-### Use Case 2: Batch Prediction cho nhiều khu vực
-1. Chuẩn bị CSV với danh sách khu vực
-2. Upload vào Dashboard → Tab "🔄 Batch Processing"
-3. Chọn model tốt nhất
-4. Start batch và theo dõi progress
-5. Download results khi hoàn thành
-
-### Use Case 3: Tạo Reports tự động
-1. Chạy training/prediction
-2. Report tự động được tạo
-3. Xem qua Dashboard hoặc `/api/reports/list`
-4. Download để chia sẻ
-
----
-
-## ⚠️ Lưu ý
-
-1. **Batch Processing**: Hiện tại chỉ xử lý tuần tự (từng job một)
-2. **Auto-retry**: Chỉ retry khi lỗi kỹ thuật, không retry nếu config sai
-3. **Charts Export**: Cần browser hỗ trợ Canvas API
-4. **Memory**: Batch lớn có thể tốn RAM, nên chia nhỏ
-
----
-
-## 🐛 Troubleshooting
-
-### Dashboard không hiển thị data
-- Kiểm tra có models/predictions trong folders chưa
-- Refresh lại trang
-- Check console log (F12)
-
-### Batch processing không chạy
-- Kiểm tra format CSV đúng chưa
-- Kiểm tra model đã chọn có tồn tại không
-- Xem API logs để debug
-
-### Charts không export được
-- Browser phải hỗ trợ Canvas.toDataURL()
-- Thử browser khác (Chrome/Firefox)
-
----
-
-## 📞 Support
-
-Nếu gặp vấn đề, check:
-1. API logs: `python api_server.py`
-2. Browser console: F12 → Console
-3. Network tab: F12 → Network
-
----
-
-**🎉 Tất cả chức năng đã được phục hồi và nâng cấp!**

PLANETARY_COMPUTER_TIPS.md

@@ -1,204 +0,0 @@
-# Microsoft Planetary Computer - Giải pháp Timeout
-
-## ❌ Vấn đề
-```
-The request exceeded the maximum allowed time
-```
-
-## ✅ Giải pháp
-
-### 1. **Giảm Parameters** (Quan trọng nhất)
-
-**Thử theo thứ tự:**
-
-```python
-# ❌ QUÁ LỚN - Dễ timeout
-bbox = [105.48, 9.77, 106.14, 10.35]  # ~70km x 60km
-start_date = "2023-01-01"
-end_date = "2023-12-31"  # 12 months
-max_scenes = 12
-```
-
-```python
-# ✅ VỪA PHẢI - Tốt
-bbox = [105.8, 10.0, 105.9, 10.1]  # ~10km x 10km
-start_date = "2024-01-01"  
-end_date = "2024-01-31"  # 1 month
-max_scenes = 5
-```
-
-```python
-# ✅ RẤT NHỎ - Luôn work
-bbox = [105.85, 10.05, 105.87, 10.07]  # ~2km x 2km
-start_date = "2024-01-15"
-end_date = "2024-01-22"  # 1 week
-max_scenes = 3
-```
-
-### 2. **Chiến lược Progressive Loading**
-
-Thay vì load toàn bộ vùng lớn 1 lúc, chia nhỏ:
-
-```python
-# Ví dụ: Chia bbox lớn thành 4 phần nhỏ
-original_bbox = [105.48, 9.77, 106.14, 10.35]
-
-# Tính mid points
-min_lon, min_lat, max_lon, max_lat = original_bbox
-mid_lon = (min_lon + max_lon) / 2
-mid_lat = (min_lat + max_lat) / 2
-
-# 4 sub-regions
-sub_regions = [
-    [min_lon, min_lat, mid_lon, mid_lat],  # Bottom-left
-    [mid_lon, min_lat, max_lon, mid_lat],  # Bottom-right
-    [min_lon, mid_lat, mid_lon, max_lat],  # Top-left
-    [mid_lon, mid_lat, max_lon, max_lat],  # Top-right
-]
-
-# Load từng region riêng, sau đó merge
-```
-
-### 3. **Tăng Timeout trong Code**
-
-Sửa `fetch_sentinel_items_with_retry`:
-
-```python
-# Thử với timeout dài hơn và ít items hơn
-for attempt in range(max_retries):
-    try:
-        # Giảm target xuống còn 2-3 items cho lần đầu
-        target_items = min(3, max_scenes) if attempt == 0 else 2
-        
-        search = catalog.search(
-            collections=["sentinel-2-l2a"],
-            bbox=bbox,
-            datetime=time_range,
-            query={"eo:cloud_cover": {"lt": cloud_cover}},
-            limit=10  # Giảm từ 20-50 xuống 10
-        )
-        
-        # Set timeout cho iterator
-        items = []
-        import signal
-        
-        def timeout_handler(signum, frame):
-            raise TimeoutError("Item fetch timeout")
-        
-        signal.signal(signal.SIGALRM, timeout_handler)
-        signal.alarm(30)  # 30 giây timeout
-        
-        try:
-            for item in search.items():
-                items.append(item)
-                if len(items) >= target_items:
-                    break
-        finally:
-            signal.alarm(0)  # Cancel alarm
-```
-
-### 4. **Alternative: Dùng Dữ liệu Local**
-
-Nếu Planetary Computer liên tục timeout:
-
-#### **a) Download trước (Recommended)**
-
-```bash
-# Dùng sentinelsat để download
-pip install sentinelsat
-
-# Download Sentinel-2 về máy
-python download_sentinel2.py --bbox 105.8,10.0,105.9,10.1 \
-    --start 2024-01-01 --end 2024-01-31
-```
-
-#### **b) Dùng Google Earth Engine** (Nếu có account)
-
-```python
-import ee
-ee.Initialize()
-
-# Load Sentinel-2 từ GEE thay vì Planetary Computer
-image = ee.ImageCollection('COPERNICUS/S2_SR') \
-    .filterBounds(ee.Geometry.Rectangle(bbox)) \
-    .filterDate(start_date, end_date) \
-    .median()
-```
-
-### 5. **Cache Aggressive**
-
-Khi đã load được data, cache ngay:
-
-```python
-# Trong prediction interface, enable cache by default
-use_cache = True  # ALWAYS
-
-# Khi load thành công, lưu cache ngay
-if items and len(items) > 0:
-    cache_file = f"cache_{bbox_hash}_{date_hash}.joblib"
-    joblib.dump({
-        'items': items,
-        's2_data': s2_data,
-        'timestamp': datetime.now()
-    }, cache_file)
-```
-
-## 🎯 **Action Plan Ngay Bây Giờ**
-
-### **Bước 1: Test với bbox CỰC NHỎ**
-
-Web interface → Prediction:
-- Min Lon: **105.80**
-- Min Lat: **10.00**
-- Max Lon: **105.82** (chỉ 0.02 độ = ~2km)
-- Max Lat: **10.02**
-- Start: **2024-01-15**
-- End: **2024-01-17** (3 ngày)
-- Max Scenes: **2**
-- Cloud Cover: 50%
-
-→ Nếu vẫn timeout → Vấn đề là internet/firewall/server PC quá tải
-
-### **Bước 2: Nếu Step 1 OK → Tăng dần**
-
-- Tăng bbox lên 0.05 độ (~5km)
-- Tăng time range lên 1 tuần
-- Tăng max_scenes lên 5
-
-### **Bước 3: Dùng Batch Processing**
-
-Thay vì 1 query lớn:
-- Chia thành nhiều queries nhỏ
-- Dùng `/api/batch/start`
-- Mỗi job = 1 vùng nhỏ
-- Merge results sau
-
-## 🔧 **Debug Commands**
-
-```bash
-# Check internet
-ping -c 3 planetarycomputer.microsoft.com
-
-# Check DNS
-nslookup planetarycomputer.microsoft.com
-
-# Test với curl
-curl -I https://planetarycomputer.microsoft.com/api/stac/v1
-
-# Monitor network
-sudo tcpdump -i any host planetarycomputer.microsoft.com
-```
-
-## 📝 **Token Info** (FYI)
-
-Microsoft Planetary Computer **KHÔNG CẦN** manual token:
-- ✅ SAS tokens tự động gen bởi `planetary_computer.sign()`
-- ✅ Auto-refresh khi cần
-- ✅ Không cần API key/registration (public access)
-- ❌ KHÔNG có "hết token" - chỉ có timeout/rate limit
-
-Nếu thấy authentication error:
-```python
-# Cài lại thư viện
-pip install --upgrade planetary-computer pystac-client
-```

SWIN_UNET_GUIDE.md

@@ -1,278 +0,0 @@
-# Hướng dẫn sử dụng Swin-UNet
-
-## Giới thiệu
-
-**Swin-UNet** là một mô hình hybrid kết hợp:
-- **Swin Transformer blocks** - cho phép học các mối quan hệ toàn cục
-- **U-Net architecture** - với skip connections để bảo toàn chi tiết địa phương
-- **Hierarchical structure** - xử lý features ở nhiều cấp độ độ phân giải
-
-## Ưu điểm chính
-
-### 1. **Kiến trúc mạnh mẽ**
-- Kết hợp được điểm mạnh của cả Transformer và CNN
-- Self-attention giúp học các mối quan hệ phức tạp
-- Skip connections bảo toàn thông tin chi tiết
-
-### 2. **Hiệu suất cao**
-- State-of-the-art accuracy cho nhiều tác vụ vision
-- Học nhanh hơn so với ViT cơ bản
-- Ổn định trong quá trình training
-
-### 3. **Linh hoạt**
-- Hoạt động tốt với ít dữ liệu (transfer learning)
-- Có thể scale lên hoặc xuống theo yêu cầu
-- Hỗ trợ cả GPU và CPU
-
-## Cấu hình tối ưu
-
-### Cấu hình nhanh (test/prototyping)
-```json
-{
-    "model_type": "swin-unet",
-    "n_estimators": 60,
-    "learning_rate": 0.001,
-    "use_gpu": true,
-    "test_size": 0.2
-}
-```
-- Training time: ~15-20 phút (GPU) / ~1-2 giờ (CPU)
-- Accuracy: Tốt cho các dataset nhỏ
-
-### Cấu hình cân bằng (production)
-```json
-{
-    "model_type": "swin-unet",
-    "n_estimators": 100,
-    "learning_rate": 0.0005,
-    "use_gpu": true,
-    "test_size": 0.2,
-    "max_scenes": 30,
-    "resolution": 10
-}
-```
-- Training time: ~30-45 phút (GPU)
-- Accuracy: Rất cao (>90% thường)
-
-### Cấu hình cao cấp (accuracy tối đa)
-```json
-{
-    "model_type": "swin-unet",
-    "n_estimators": 150,
-    "learning_rate": 0.0003,
-    "use_gpu": true,
-    "test_size": 0.2,
-    "max_scenes": 60,
-    "resolution": 10
-}
-```
-- Training time: ~45-60 phút (GPU)
-- Accuracy: Tối ưu nhất (95%+)
-- Yêu cầu: Dataset lớn, GPU mạnh
-
-## So sánh với các model khác
-
-| Tiêu chí | CNN | ResNet | ViT | **Swin-UNet** |
-|---------|-----|--------|-----|--------------|
-| Độ chính xác | ⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ |
-| Tốc độ training | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐ | ⭐⭐⭐⭐ |
-| Bộ nhớ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐ | ⭐⭐⭐ |
-| Ổn định | ⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ |
-| Dataset nhỏ | ✓ | ✓ | ✗ | ✓ |
-| Dataset lớn | ✓ | ✓ | ✓ | ✓ |
-
-## Kiến trúc chi tiết
-
-### Encoder (Đường xuống)
-```
-Input Features (n_features)
-        ↓
-    Adapter Layer (project to embed_dim)
-        ↓
-    Encoder1 (embed_dim → embed_dim)
-        ↓
-   Downsample (→ embed_dim*2)
-        ↓
-    Encoder2 (embed_dim*2 → embed_dim*2)
-        ↓
-   Downsample (→ embed_dim*4)
-        ↓
-    Encoder3 (embed_dim*4) - Bottleneck
-```
-
-### Decoder (Đường lên)
-```
-    Encoder3 Output
-        ↓
-   Upsample (→ embed_dim*2)
-        ↓
-  Concatenate with Skip from Encoder2
-        ↓
-    Decoder2 (embed_dim*4 → embed_dim*2)
-        ↓
-   Upsample (→ embed_dim)
-        ↓
-  Concatenate with Skip from Encoder1
-        ↓
-    Decoder1 (embed_dim*2 → embed_dim)
-        ↓
-  Attention Layer (Multi-head)
-        ↓
-   Classifier (embed_dim → n_classes)
-```
-
-### Hyperparameters
-- **embed_dim**: 128 (kích thước embedding)
-- **batch_size**: 32
-- **optimizer**: AdamW (với weight decay = 0.01)
-- **scheduler**: CosineAnnealingLR
-- **dropout**: 0.1-0.3 (để regularization)
-
-## Kỹ thuật training
-
-### 1. Learning Rate Schedule
-- Bắt đầu từ `learning_rate`
-- Giảm dần theo cosine schedule
-- Giúp convergence tốt hơn
-
-### 2. Weight Decay
-- Sử dụng AdamW với weight_decay=0.01
-- Ngăn overfitting
-- Improve generalization
-
-### 3. Attention Mechanism
-- Multi-head attention (4 heads)
-- Giúp model học các mối quan hệ phức tạp
-- Cộng hưởng với self-attention trong Transformer
-
-## Tips để đạt kết quả tốt
-
-### ✅ Làm gì
-1. **Tăng epochs** - Swin-UNet thường cần nhiều epochs (60-150)
-2. **Sử dụng GPU** - Training nhanh hơn 10-20x
-3. **Learning rate nhỏ** - 0.0001 - 0.0005 cho dataset lớn
-4. **Augmentation** - Nếu có thể, augment training data
-5. **Monitor loss** - Loss nên giảm dần qua epochs
-
-### ❌ Tránh gì
-1. **Learning rate quá cao** - Training không ổn định
-2. **Quá ít epochs** - Model chưa hội tụ
-3. **Batch size quá lớn** - Hết bộ nhớ
-4. **Overfitting** - Nếu train_acc >> test_acc, cần giảm epochs
-
-## Troubleshooting
-
-### Vấn đề: "CUDA out of memory"
-```python
-# Giải pháp:
-- Giảm batch_size (từ 32 xuống 16)
-- Giảm embed_dim (từ 128 xuống 64)
-- Sử dụng CPU: "use_gpu": false
-```
-
-### Vấn đề: Loss không giảm
-```python
-# Giải pháp:
-- Giảm learning_rate (thử 0.0001)
-- Tăng epochs (thử 150+)
-- Kiểm tra dữ liệu training
-```
-
-### Vấn đề: Quá chậm
-```python
-# Giải pháp:
-- Giảm n_estimators (↓ epochs)
-- Giảm max_scenes (↓ dữ liệu)
-- Sử dụng GPU nếu có
-```
-
-### Vấn đề: Accuracy thấp
-```python
-# Giải pháp:
-- Tăng epochs (thử 100-150)
-- Thử learning_rate khác (0.0005, 0.001)
-- Kiểm tra chất lượng dữ liệu training
-- Thử model khác (ViT)
-```
-
-## So sánh Learning Rates
-
-| Learning Rate | Độ nhanh | Ổn định | Khuyến cáo |
-|---------------|----------|---------|-----------|
-| 0.01 | Nhanh | Kém | ❌ Quá cao |
-| 0.005 | Trung bình | Trung bình | ⚠️ Có thể dùng |
-| 0.001 | Trung bình | Tốt | ✅ Mặc định |
-| 0.0005 | Chậm | Rất tốt | ✅ Dùng khi cần độ chính xác cao |
-| 0.0001 | Rất chậm | Tuyệt | ✅ Cho ViT/LoRA |
-
-## Khi nào dùng Swin-UNet?
-
-### ✓ Sử dụng khi
-- Bạn có dataset vừa đến lớn (500+ samples)
-- Cần độ chính xác cao (>90%)
-- Có GPU hoặc thời gian chờ đợi
-- Muốn model ổn định và đáng tin cậy
-- Dữ liệu có các mẫu phức tạp
-
-### ✗ Không sử dụng khi
-- Dataset rất nhỏ (<200 samples) → Dùng CNN hoặc XGBoost
-- Thời gian quá hạn → Dùng CNN hoặc XGBoost
-- Không có GPU và thời gian bị giới hạn → Dùng XGBoost
-- Cần mô hình hết sức nhẹ → Dùng CNN
-
-## Ví dụ thực tế
-
-### Trường hợp 1: Phân loại nhanh
-```json
-{
-    "model_type": "swin-unet",
-    "n_estimators": 60,
-    "learning_rate": 0.001,
-    "use_gpu": true,
-    "max_scenes": 12,
-    "resolution": 20
-}
-```
-**Kết quả**: ~15 phút, 85% accuracy
-
-### Trường hợp 2: Phân loại cân bằng
-```json
-{
-    "model_type": "swin-unet",
-    "n_estimators": 100,
-    "learning_rate": 0.0005,
-    "use_gpu": true,
-    "max_scenes": 30,
-    "resolution": 10
-}
-```
-**Kết quả**: ~40 phút, 92% accuracy
-
-### Trường hợp 3: Phân loại chính xác tối đa
-```json
-{
-    "model_type": "swin-unet",
-    "n_estimators": 150,
-    "learning_rate": 0.0003,
-    "use_gpu": true,
-    "max_scenes": 60,
-    "resolution": 10
-}
-```
-**Kết quả**: ~60 phút, 96%+ accuracy
-
-## Tài liệu tham khảo
-
-- Swin Transformer: https://arxiv.org/abs/2103.14030
-- U-Net: https://arxiv.org/abs/1505.04597
-- Swin-UNet for Medical Image: https://arxiv.org/abs/2105.05537
-
-## Kết luận
-
-Swin-UNet là lựa chọn tuyệt vời khi bạn cần:
-- ✅ Độ chính xác cao
-- ✅ Model ổn định
-- ✅ Khả năng xử lý dữ liệu phức tạp
-- ✅ Training tương đối nhanh
-
-Hãy thử Swin-UNet cho các tác vụ classification quan trọng và cần chất lượng cao!

SYSTEM_UPDATE_GUIDE.md

@@ -1,228 +0,0 @@
-# HƯỚNG DẪN SỬ DỤNG HỆ THỐNG MỚI
-
-## Tổng quan
-
-Hệ thống đã được cập nhật để chuẩn hóa việc trích xuất features giữa training và prediction, sử dụng module `feature_extractor.py`.
-
-## Các thành phần mới
-
-### 1. feature_extractor.py
-Module chuẩn hóa việc trích xuất features với 3 modes:
-
-- **simple**: 3 features cơ bản
-  - NDVI_mean
-  - VH_db_mean
-  - VV_db_mean
-  
-- **temporal**: 39+ features time-series
-  - NDVI_t1, NDVI_t2, ..., NDVI_tn
-  - NDWI_t1, NDWI_t2, ..., NDWI_tn
-  - NDBI_t1, NDBI_t2, ..., NDBI_tn
-  - VH_db_mean, VV_db_mean, VH_VV_ratio
-  
-- **extended**: 15 features với statistics
-  - NDVI_mean, NDVI_std, NDVI_min, NDVI_max
-  - NDWI_mean, NDWI_std, NDWI_min, NDWI_max
-  - NDBI_mean, NDBI_std, NDBI_min, NDBI_max
-  - VH_db_mean, VV_db_mean, VH_VV_ratio
-
-### 2. train_module.py (Đã cập nhật)
-- Thêm tham số `feature_mode` (default='simple')
-- Sử dụng FeatureExtractor để extract features
-- Lưu `feature_mode` vào metadata của model
-- Load đúng bands Sentinel-2 theo feature mode
-
-### 3. api_server.py (Cần cập nhật thủ công)
-File này quá lớn để tự động replace. Cần thay thế hàm `run_prediction` bằng version mới trong `run_prediction_new.py`.
-
-## Cách sử dụng
-
-### Training với feature modes khác nhau
-
-#### 1. Simple Mode (Mặc định - Nhanh nhất)
-```python
-from train_module import train_model
-
-result = train_model(
-    bbox=[105.6, 9.3, 106.2, 9.8],
-    time_range='2023-03-01/2023-05-31',
-    max_scenes=12,
-    feature_mode='simple',  # 3 features
-    model_type='xgboost',
-    use_cache=True
-)
-```
-
-#### 2. Temporal Mode (Cho model_odc.joblib)
-```python
-result = train_model(
-    bbox=[105.6, 9.3, 106.2, 9.8],
-    time_range='2023-03-01/2023-05-31',
-    max_scenes=12,
-    feature_mode='temporal',  # 39+ features
-    model_type='random_forest',
-    use_cache=True
-)
-```
-
-#### 3. Extended Mode (Cân bằng speed/accuracy)
-```python
-result = train_model(
-    bbox=[105.6, 9.3, 106.2, 9.8],
-    time_range='2023-03-01/2023-05-31',
-    max_scenes=12,
-    feature_mode='extended',  # 15 features
-    model_type='xgboost',
-    use_cache=True
-)
-```
-
-### Prediction
-Prediction sẽ tự động detect feature_mode từ model metadata và sử dụng FeatureExtractor tương ứng.
-
-```python
-# Prediction sẽ tự động:
-# 1. Load model metadata
-# 2. Đọc feature_mode từ metadata
-# 3. Khởi tạo FeatureExtractor với mode tương ứng
-# 4. Extract features giống như training
-# 5. Predict
-```
-
-## Tạo metadata cho model_odc.joblib
-
-Model hiện tại `model_odc.joblib` được train với 39 features (temporal mode) nhưng chưa có metadata. Tạo metadata:
-
-```bash
-python create_odc_metadata.py
-```
-
-File này sẽ tạo `model_train/model_odc_info.json` với:
-- n_features: 39
-- feature_mode: "temporal"
-- features: list of 39 feature names
-
-## So sánh các modes
-
-| Feature Mode | N Features | Training Time | Accuracy | Use Case |
-|-------------|-----------|---------------|----------|----------|
-| simple | 3 | Nhanh nhất | Trung bình | Test nhanh, dataset nhỏ |
-| extended | 15 | Trung bình | Tốt | Cân bằng speed/accuracy |
-| temporal | 39+ | Chậm nhất | Tốt nhất | Production, dataset lớn |
-
-## Lưu ý quan trọng
-
-### 1. Bands được load
-- **simple**: B04, B08, SCL
-- **temporal/extended**: B02, B03, B04, B08, B11, SCL
-
-### 2. Cache compatibility
-Cache cũ từ trước khi cập nhật sẽ KHÔNG tương thích vì:
-- Không có field `feature_mode`
-- Features có thể không match
-
-**Giải pháp**: Xóa cache cũ
-```bash
-rm -rf dataset_cache/*
-```
-
-### 3. Model compatibility
-- Models cũ (trước cập nhật) sẽ được coi là `feature_mode='simple'` nếu không có metadata
-- Models mới sẽ có field `feature_mode` trong metadata
-
-## Workflow đề xuất
-
-### Bước 1: Xóa cache cũ
-```bash
-rm -rf dataset_cache/*
-```
-
-### Bước 2: Tạo metadata cho model_odc.joblib
-```bash
-python create_odc_metadata.py
-```
-
-### Bước 3: Cập nhật api_server.py
-Thay thế hàm `run_prediction` (line 834-1295) với nội dung từ `run_prediction_new.py`
-
-### Bước 4: Test training với simple mode
-```bash
-# Qua web interface hoặc
-python test_training_simple.py
-```
-
-### Bước 5: Test prediction với model vừa train
-```bash
-# Qua web interface
-# Model sẽ tự động detect feature_mode và extract đúng features
-```
-
-### Bước 6: Test với temporal mode (nếu cần accuracy cao)
-```bash
-python test_training_temporal.py
-```
-
-## Troubleshooting
-
-### Lỗi: "feature_mode not found in metadata"
-- Model cũ chưa có metadata
-- **Giải pháp**: Hệ thống tự động fallback về 'simple' mode
-
-### Lỗi: "Expected X features but got Y"
-- Feature extraction không match với training
-- **Giải pháp**: Kiểm tra model metadata, đảm bảo feature_mode đúng
-
-### Lỗi: "B11 band not found"
-- Sentinel-2 scene thiếu SWIR band
-- **Giải pháp**: Hệ thống tự động fallback về B02
-
-## API Changes
-
-### TrainingConfig (Mới)
-```python
-class TrainingConfig(BaseModel):
-    # ... existing fields ...
-    feature_mode: str = "simple"  # NEW: 'simple', 'temporal', 'extended'
-```
-
-### Model Metadata (Mới)
-```json
-{
-  "feature_mode": "temporal",
-  "features": ["NDVI_t1", "NDVI_t2", ...],
-  "n_features": 39,
-  ...
-}
-```
-
-## File Structure
-
-```
-/home/x79/remote-sensing/
-├── feature_extractor.py         # NEW: Core feature extraction module
-├── train_module.py              # UPDATED: Uses FeatureExtractor
-├── api_server.py                # NEEDS UPDATE: run_prediction function
-├── run_prediction_new.py        # NEW: Updated run_prediction code
-├── create_odc_metadata.py       # NEW: Generate metadata for model_odc.joblib
-├── SYSTEM_UPDATE_GUIDE.md       # This file
-└── model_train/
-    ├── model_odc.joblib         # Existing 39-feature model
-    ├── model_odc_info.json      # TO CREATE: Metadata file
-    └── ...
-```
-
-## Next Steps
-
-1. ✅ Created feature_extractor.py
-2. ✅ Updated train_module.py
-3. ⏳ Update api_server.py (manual)
-4. ⏳ Create metadata for model_odc.joblib
-5. ⏳ Test full workflow
-
-## Contact & Support
-
-Nếu gặp vấn đề, kiểm tra:
-1. feature_extractor.py có import được không
-2. Model metadata có field `feature_mode` chưa
-3. Cache đã được xóa chưa
-4. api_server.py đã cập nhật run_prediction chưa

TRAINING_UPDATE_SUMMARY.md

@@ -1,283 +0,0 @@
-# Tóm tắt cập nhật Training Interface & API
-
-## 📋 Những gì đã cập nhật
-
-### 1. **Backend API (api_server.py)**
-
-#### ✅ Cập nhật giá trị mặc định từ 01.train_ODC.ipynb:
-- **Bbox mới**: `[105.5, 9.2, 106.4, 10.0]` (thay vì `[105.6, 9.3, 106.2, 9.8]`)
-- **Thời gian mới**: `2023-03-01` → `2023-12-31` (thay vì `2023-03-01` → `2023-05-31`)
-
-#### ✅ Thêm Label Mapping Constants:
-```python
-DEFAULT_LABEL_MAPPING = {
-    "Lua tom": "0",
-    "Lua": "1", 
-    "CHN": "2",
-    "CLN": "3",
-    "TS": "4",
-    "Song": "5",
-    "Dat xay dung": "6",
-    "Rung": "7",
-}
-```
-
-#### ✅ API Endpoints mới:
-
-**1. `GET /api/training/labels`**
-- Trả về danh sách tất cả labels và label mapping
-- Response:
-```json
-{
-  "label_mapping": {...},
-  "label_names": {...},
-  "count": 8,
-  "labels": [...]
-}
-```
-
-**2. `GET /api/training/files`**
-- List tất cả shapefile trong thư mục `/train`
-- Hiển thị: filename, size, số điểm, label column, unique labels
-- Response:
-```json
-{
-  "files": [
-    {
-      "filename": "ST_training data_updated_1130points_new.shp",
-      "path": "train/...",
-      "size_mb": 0.15,
-      "point_count": 1130,
-      "label_column": "Hientrang",
-      "unique_labels": [...],
-      "label_count": 8
-    }
-  ],
-  "count": 2,
-  "directory": "train/"
-}
-```
-
-**3. `GET /api/training/shapefile/{filename}/labels`**
-- Đọc chi tiết labels từ một shapefile cụ thể
-- Trả về: số điểm, unique labels, label counts, bbox, columns
-- Response:
-```json
-{
-  "filename": "...",
-  "label_column": "Hientrang",
-  "point_count": 1130,
-  "unique_labels": [...],
-  "label_count": 8,
-  "labels": [
-    {
-      "name": "Lua tom",
-      "code": "0",
-      "count": 150,
-      "mapped": true
-    },
-    ...
-  ],
-  "bbox": [105.5, 9.2, 106.4, 10.0],
-  "columns": [...]
-}
-```
-
-#### ✅ Cập nhật Presets:
-- Preset 1: "PC - Nhỏ" với bbox mới
-- Preset 2: "Server - Trung bình" với bbox mới
-- Preset 3: "Full - ODC" - PRESET MỚI từ 01.train_ODC.ipynb
-  - Bbox: `[105.5, 9.2, 106.4, 10.0]`
-  - Time: `2023-03-01` → `2023-12-31`
-  - Max scenes: 1
-  - Resolution: 10m
-
----
-
-### 2. **Frontend UI (training_interface.html)**
-
-#### ✅ Cập nhật giá trị mặc định trong form:
-- **Hidden inputs bbox**: 
-  - `minLon: 105.5, minLat: 9.2, maxLon: 106.4, maxLat: 10.0`
-- **Date inputs**:
-  - `startDate: 2023-03-01, endDate: 2023-12-31`
-
-#### ✅ Thêm section "Training Data (Shapefile)":
-```html
-<h3>📊 Training Data (Shapefile)</h3>
-<select id="trainingShapefile">...</select>
-```
-
-Features:
-- Dropdown chọn shapefile từ thư mục `/train`
-- Tự động load default: `ST_training data_updated_1130points_new.shp`
-- Hiển thị thông tin: số điểm, label column, số lớp, bbox
-
-#### ✅ Thêm phần hiển thị thông tin Shapefile:
-```html
-<div id="shapefileInfo">
-  - Số điểm
-  - Label column
-  - Số lớp
-  - Bbox
-  - Phân bố labels (với icon ✅/⚠️)
-  - Button "Áp dụng Bbox từ Shapefile"
-</div>
-```
-
-#### ✅ JavaScript Functions mới:
-
-**1. `loadTrainingFiles()`**
-- Load danh sách shapefile từ API
-- Populate dropdown
-- Auto-select default shapefile
-
-**2. `loadShapefileLabels(filename)`**
-- Load chi tiết labels từ shapefile
-- Hiển thị phân bố labels
-- Highlight labels đã map vs chưa map
-
-**3. `applyShapefileBbox()`**
-- Áp dụng bbox từ shapefile đã chọn
-- Cập nhật form inputs
-- Vẽ rectangle trên map
-- Hiển thị notification
-
-**4. `showNotification(type, message)`**
-- Helper function để hiển thị notifications
-- Support types: success, error, warning
-
-#### ✅ Cập nhật form submission:
-- Thêm `training_shapefile` vào config
-- Default: `train/ST_training data_updated_1130points_new.shp`
-
-#### ✅ Event listeners:
-```javascript
-document.getElementById('trainingShapefile').addEventListener('change', 
-  (e) => loadShapefileLabels(e.target.value)
-);
-```
-
----
-
-### 3. **Bản đồ (Map)**
-
-#### ✅ Initial rectangle với bbox mới:
-- Tự động vẽ rectangle với bbox từ backend
-- Fit map bounds để hiển thị khu vực
-
-#### ✅ Dynamic update từ shapefile:
-- Khi chọn shapefile → có thể áp dụng bbox
-- Màu khác biệt (xanh dương) để dễ nhận biết
-
----
-
-## 🧪 Test Script
-
-File `test_training_api.py` để test các endpoints:
-
-```bash
-# Run API server (terminal 1)
-conda activate env_01
-python api_server.py
-
-# Run test script (terminal 2)
-conda activate env_01
-python test_training_api.py
-```
-
-Test coverage:
-1. ✅ GET /api/training/labels
-2. ✅ GET /api/training/files
-3. ✅ GET /api/training/shapefile/{filename}/labels
-4. ✅ GET /api/config/presets
-
----
-
-## 📊 Workflow mới
-
-### Cách sử dụng trên giao diện:
-
-1. **Mở Training Interface**: http://localhost:8000/training
-
-2. **Chọn Training Data**:
-   - Chọn shapefile từ dropdown "📊 Training Data"
-   - Xem thông tin: số điểm, labels, bbox
-   - (Optional) Click "📍 Áp dụng Bbox từ Shapefile"
-
-3. **Chọn Khu vực**:
-   - Option 1: Chọn tỉnh thành
-   - Option 2: Vẽ rectangle trên map
-   - Option 3: Áp dụng bbox từ shapefile
-   - Option 4: Chọn preset
-
-4. **Cấu hình thời gian và parameters**:
-   - Thời gian mặc định: 2023-03-01 → 2023-12-31
-   - Bbox mặc định: [105.5, 9.2, 106.4, 10.0]
-
-5. **Start Training**:
-   - Form tự động gửi `training_shapefile` parameter
-   - Backend sẽ dùng đúng shapefile đã chọn
-
----
-
-## 🎯 Kết quả
-
-### ✅ Backend:
-- 3 API endpoints mới hoạt động
-- Default values khớp với notebook
-- Label mapping được share
-
-### ✅ Frontend:
-- UI mới để chọn shapefile
-- Hiển thị chi tiết labels
-- Auto-load default shapefile
-- Bbox từ shapefile có thể áp dụng
-
-### ✅ Map:
-- Initial bbox khớp với backend
-- Update bbox từ nhiều nguồn
-- Visual feedback rõ ràng
-
----
-
-## 🔍 Debug & Verify
-
-### Check API:
-```bash
-# List training files
-curl http://localhost:8000/api/training/files
-
-# Get labels
-curl http://localhost:8000/api/training/labels
-
-# Get shapefile labels
-curl "http://localhost:8000/api/training/shapefile/ST_training data_updated_1130points_new.shp/labels"
-```
-
-### Check Browser Console:
-- F12 → Console
-- Xem logs khi chọn shapefile
-- Check network requests
-
----
-
-## 📝 Notes
-
-1. **Training shapefile path format**:
-   - Frontend select value: `ST_training data_updated_1130points_new.shp`
-   - Backend receives: `train/ST_training data_updated_1130points_new.shp`
-   - Auto-prepend `train/` prefix in form submission
-
-2. **Label mapping**:
-   - ✅ icon: Label có trong DEFAULT_LABEL_MAPPING
-   - ⚠️ icon: Label chưa có trong mapping
-
-3. **Bbox sources**:
-   - Default từ backend
-   - Từ tỉnh thành
-   - Từ shapefile
-   - Từ preset
-   - Vẽ thủ công
-
-Tất cả đều hoạt động đồng bộ!

ThuanHoa/.ipynb_checkpoints/Untitled-checkpoint.ipynb

@@ -1,10 +1,3 @@
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- "nbformat": 4,
- "nbformat_minor": 5
-}
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UPDATE_COMPLETE.md

@@ -1,254 +0,0 @@
-# 🎉 Hệ thống đã được cập nhật hoàn chỉnh!
-
-## 📁 Cấu trúc hệ thống mới
-
-```
-remote-sensing/
-├── index.html                      # 🆕 Trang chính với tab navigation
-├── training_interface.html         # ✅ Interface training (độc lập)
-├── prediction_interface.html       # 🆕 Interface prediction (tách riêng)
-├── dashboard.html                  # ✅ Dashboard visualization
-├── api_server.py                   # ✅ API server (đã cập nhật đầy đủ)
-├── train_module.py                 # Training logic
-├── report_generator.py             # Auto report generator
-├── batch_regions_example.csv       # 🆕 CSV mẫu cho batch processing
-├── NEW_FEATURES.md                 # Documentation
-└── test_new_features.py            # Test script
-```
-
-## 🚀 Các URL hiện tại
-
-### Main Pages
-- **Trang chủ với tabs**: http://localhost:8000/
-- **Training standalone**: http://localhost:8000/training
-- **Prediction standalone**: http://localhost:8000/prediction
-- **Dashboard standalone**: http://localhost:8000/dashboard
-- **API Docs**: http://localhost:8000/docs
-
-### Tab Navigation trong Index
-1. 🏠 **Trang Chủ** - Tổng quan & quick start
-2. 🎓 **Training** - Training interface (iframe)
-3. 🗺️ **Prediction** - Prediction interface (iframe)
-4. 📊 **Dashboard** - Visualization & charts
-5. 🤖 **Models** - Quản lý models
-6. 📄 **Reports** - Xem & download reports
-7. 🔄 **Batch Processing** - Batch prediction queue
-
-## ✨ Chức năng đã cập nhật
-
-### 1. Tab Navigation System
-- ✅ Giao diện thống nhất với 7 tabs
-- ✅ Smooth transition animations
-- ✅ Responsive design
-- ✅ Real-time data loading
-
-### 2. Training Interface (Tách riêng)
-- ✅ Có thể truy cập độc lập tại `/training`
-- ✅ Hoặc embed trong tab của index.html
-- ✅ Đầy đủ chức năng như cũ
-
-### 3. Prediction Interface (Mới tách riêng)
-- ✅ Giao diện riêng biệt tại `/prediction`
-- ✅ Map selector với Leaflet
-- ✅ Model dropdown với info preview
-- ✅ Time & data configuration
-- ✅ Real-time status tracking
-- ✅ Download results & view reports
-- ✅ History của tất cả predictions
-
-### 4. Dashboard & Visualization
-- ✅ Accuracy trends charts
-- ✅ F1-Score comparison
-- ✅ Class distribution
-- ✅ Export PNG/PDF
-- ✅ Real-time statistics
-
-### 5. Batch Processing
-- ✅ Upload CSV file
-- ✅ Auto-retry mechanism
-- ✅ Queue management
-- ✅ Progress tracking
-- ✅ Real-time status updates
-
-## 🔧 API Endpoints mới
-
-### Dashboard APIs
-```
-GET /api/dashboard/accuracy-trends        # Accuracy trends over time
-GET /api/dashboard/statistics             # Tổng quan thống kê
-GET /api/dashboard/class-distribution/{model}  # Phân bố classes
-```
-
-### Batch Processing APIs
-```
-POST /api/batch/start                     # Bắt đầu batch prediction
-GET  /api/batch/status                    # Kiểm tra queue status
-GET  /api/batch/results/{batch_id}        # Lấy kết quả batch
-POST /api/batch/cancel/{batch_id}         # Hủy batch
-```
-
-### Existing APIs (đã có)
-```
-# Training
-POST /api/training/start
-GET  /api/training/status
-POST /api/training/stop
-
-# Prediction
-POST /api/prediction/start
-GET  /api/prediction/status
-
-# Models
-GET  /api/models/list
-
-# Reports
-GET  /api/reports/list
-GET  /api/reports/view/{filename}
-GET  /api/reports/download/{filename}
-
-# Predictions
-GET  /api/predictions/list
-GET  /api/predictions/download/{filename}
-
-# Cache
-GET  /api/cache/info
-POST /api/cache/clear
-```
-
-## 🎯 Cách sử dụng
-
-### 1. Khởi động server
-```bash
-conda activate env_01
-python api_server.py
-```
-
-### 2. Truy cập hệ thống
-Mở browser: http://localhost:8000/
-
-### 3. Workflow cơ bản
-
-#### A. Training
-1. Click tab "🎓 Training"
-2. Vẽ bbox hoặc chọn preset
-3. Cấu hình model type, parameters
-4. Click "Start Training"
-5. Theo dõi progress
-6. Download model & view report
-
-#### B. Prediction
-1. Click tab "🗺️ Prediction"
-2. Chọn model đã train
-3. Vẽ bbox khu vực cần predict
-4. Cấu hình time range & data
-5. Click "Start Prediction"
-6. Download GeoTIFF khi hoàn thành
-
-#### C. Dashboard
-1. Click tab "📊 Dashboard"
-2. Xem accuracy trends
-3. So sánh models
-4. Export charts PNG/PDF
-
-#### D. Batch Processing
-1. Click tab "🔄 Batch Processing"
-2. Upload CSV file (xem batch_regions_example.csv)
-3. Chọn model
-4. Click "Start Batch Prediction"
-5. Theo dõi progress từng job
-
-## 📊 Format CSV cho Batch Processing
-
-```csv
-name,min_lon,min_lat,max_lon,max_lat,start_date,end_date,max_scenes,cloud_cover,resolution
-Region_1,105.6,9.3,105.8,9.5,2023-03-01,2023-05-31,12,30,20
-Region_2,105.8,9.3,106.0,9.5,2023-03-01,2023-05-31,12,30,20
-```
-
-## 🔍 Test các chức năng
-
-```bash
-# Test tất cả APIs
-python test_new_features.py
-
-# Hoặc test thủ công
-curl http://localhost:8000/api/dashboard/statistics
-curl http://localhost:8000/api/models/list
-curl http://localhost:8000/api/batch/status
-```
-
-## 📝 Notes
-
-### Import Warnings
-Các warning về import (xarray, numpy, etc.) là bình thường vì:
-- Các thư viện này được import động trong runtime
-- Chỉ khi thực sự cần thiết (prediction/training)
-- Không ảnh hưởng đến hoạt động của server
-
-### Browser Compatibility
-- Khuyến nghị: Chrome, Firefox, Edge (latest)
-- Mobile responsive: Đã optimize
-- Chart.js & Leaflet: CDN loaded automatically
-
-### Performance
-- Training: Tùy vào config (5-30 phút)
-- Prediction: 2-10 phút tùy khu vực
-- Batch: Sequential processing (1 job/time)
-- Dashboard: Real-time updates mỗi 3s
-
-## 🎨 Tính năng UI/UX
-
-### Design
-- ✅ Modern gradient backgrounds
-- ✅ Card-based layouts
-- ✅ Smooth animations
-- ✅ Consistent color scheme
-- ✅ Responsive grid system
-
-### Interactions
-- ✅ Real-time progress bars
-- ✅ Status badges
-- ✅ Loading spinners
-- ✅ Error alerts
-- ✅ Success notifications
-
-### Charts
-- ✅ Interactive tooltips
-- ✅ Zoom & pan
-- ✅ Export functionality
-- ✅ Responsive sizing
-
-## 🚨 Troubleshooting
-
-### Server không start
-```bash
-# Check port 8000
-lsof -i :8000
-# Kill if needed
-kill -9 <PID>
-```
-
-### Tab không load
-- Clear browser cache
-- Check console (F12)
-- Verify file paths
-
-### Batch không chạy
-- Check CSV format
-- Verify model exists
-- Check API logs
-
-## 📞 Support
-
-Nếu gặp vấn đề:
-1. Check terminal logs
-2. Check browser console (F12)
-3. Verify all HTML files exist
-4. Test API endpoints với curl/Postman
-
----
-
-**🎉 Hệ thống đã sẵn sàng sử dụng!**
-
-Start server: `python api_server.py`
-Access: http://localhost:8000/

UPDATE_SUMMARY.md

@@ -1,263 +0,0 @@
-# CẬP NHẬT HỆ THỐNG HOÀN TẤT
-
-## ✅ ĐÃ HOÀN THÀNH
-
-### 1. Tạo module Feature Extractor chuẩn
-**File**: `feature_extractor.py`
-
-Module này chuẩn hóa việc trích xuất features với 3 modes:
-
-#### Mode 'simple' (3 features - Nhanh nhất)
-```python
-features = [
-    'NDVI_mean',
-    'VH_db_mean', 
-    'VV_db_mean'
-]
-```
-
-#### Mode 'temporal' (39 features - Cho model_odc.joblib)
-```python
-features = [
-    'NDVI_t1', 'NDVI_t2', ..., 'NDVI_t12',  # 12 timesteps
-    'NDWI_t1', 'NDWI_t2', ..., 'NDWI_t12',  # 12 timesteps
-    'NDBI_t1', 'NDBI_t2', ..., 'NDBI_t12',  # 12 timesteps
-    'VH_db_mean', 'VV_db_mean', 'VH_VV_ratio'  # 3 radar
-]
-# Total: 12 + 12 + 12 + 3 = 39 features
-```
-
-#### Mode 'extended' (15 features - Cân bằng)
-```python
-features = [
-    'NDVI_mean', 'NDVI_std', 'NDVI_min', 'NDVI_max',
-    'NDWI_mean', 'NDWI_std', 'NDWI_min', 'NDWI_max',
-    'NDBI_mean', 'NDBI_std', 'NDBI_min', 'NDBI_max',
-    'VH_db_mean', 'VV_db_mean', 'VH_VV_ratio'
-]
-```
-
-### 2. Cập nhật Training Module
-**File**: `train_module.py`
-
-**Thay đổi chính**:
-- ✅ Thêm parameter `feature_mode` vào hàm `train_model()`
-- ✅ Import và sử dụng `FeatureExtractor`
-- ✅ Load đúng Sentinel-2 bands theo feature mode:
-  - simple: B04, B08, SCL
-  - temporal/extended: B02, B03, B04, B08, B11, SCL
-- ✅ Lưu `feature_mode` vào model metadata
-- ✅ Lưu danh sách feature names chính xác vào metadata
-
-**Cách sử dụng**:
-```python
-from train_module import train_model
-
-# Training với simple mode (mặc định)
-result = train_model(
-    bbox=[105.6, 9.3, 106.2, 9.8],
-    time_range='2023-03-01/2023-05-31',
-    feature_mode='simple',  # Thêm parameter này
-    model_type='xgboost'
-)
-
-# Training với temporal mode (cho model 39 features)
-result = train_model(
-    bbox=[105.6, 9.3, 106.2, 9.8],
-    time_range='2023-03-01/2023-05-31',
-    feature_mode='temporal',  # Temporal mode
-    model_type='random_forest'
-)
-```
-
-### 3. Tạo metadata cho model_odc.joblib
-**File**: `model_train/model_odc_info.json` (đã tạo)
-
-Metadata này chứa:
-- `feature_mode`: "temporal"
-- `n_features`: 39
-- `features`: danh sách 39 feature names đầy đủ
-- 8 class names: Lua tom, Lua, CHN, CLN, TS, Song, Dat xay dung, Rung
-
-**Verification**:
-```bash
-cat model_train/model_odc_info.json | grep feature_mode
-# Output: "feature_mode": "temporal"
-```
-
-### 4. Hướng dẫn sử dụng
-**File**: `SYSTEM_UPDATE_GUIDE.md`
-
-Document đầy đủ về:
-- Cách sử dụng các feature modes
-- So sánh performance giữa các modes
-- Troubleshooting
-- API changes
-
-### 5. Updated prediction code
-**File**: `run_prediction_new.py`
-
-Chứa code mới cho hàm `run_prediction()` sử dụng `FeatureExtractor`.
-
-## 🔧 CẦN LÀM TIẾP
-
-### 1. Cập nhật api_server.py (Thủ công)
-**Cần thay thế hàm `run_prediction` (line 834+)**
-
-**Lý do không tự động**: Hàm quá dài, file api_server.py quá lớn (3000+ lines)
-
-**Cách làm**:
-1. Mở `api_server.py`
-2. Tìm hàm `async def run_prediction(config: PredictionConfig):`
-3. Copy toàn bộ code từ `run_prediction_new.py`
-4. Paste thay thế hàm cũ
-
-**Hoặc sử dụng editor**:
-```python
-# Tìm line bắt đầu:
-async def run_prediction(config: PredictionConfig):
-    """Chạy prediction process - Áp dụng phương pháp từ 02.predict_ODC.ipynb"""
-
-# Thay thế toàn bộ hàm (đến hết try-except) bằng code từ run_prediction_new.py
-```
-
-### 2. Test toàn bộ hệ thống
-
-#### Test 1: Training với simple mode
-```bash
-# Via web interface hoặc
-curl -X POST http://localhost:8000/api/training/start \
-  -H "Content-Type: application/json" \
-  -d '{
-    "feature_mode": "simple",
-    "model_type": "xgboost",
-    "bbox": [105.6, 9.3, 106.2, 9.8],
-    ...
-  }'
-```
-
-#### Test 2: Prediction với model vừa train
-```bash
-# Model sẽ tự động detect feature_mode từ metadata
-curl -X POST http://localhost:8000/api/prediction/start \
-  -H "Content-Type: application/json" \
-  -d '{
-    "model_filename": "model_xgboost_20251223_120000.joblib"
-  }'
-```
-
-#### Test 3: Prediction với model_odc.joblib
-```bash
-# Model có metadata với feature_mode='temporal'
-# Prediction sẽ tự động extract 39 temporal features
-curl -X POST http://localhost:8000/api/prediction/start \
-  -H "Content-Type: application/json" \
-  -d '{
-    "model_filename": "model_odc.joblib"
-  }'
-```
-
-## 📊 KẾT QUẢ MONG ĐỢI
-
-### Trước khi cập nhật:
-- ❌ Training tạo 3 features: NDVI_mean, VH, VV
-- ❌ Prediction cố extract 39 features
-- ❌ Mismatch: Model expects 39 but got 3
-- ❌ Lỗi: "StandardScaler expects 39 features"
-
-### Sau khi cập nhật:
-- ✅ Training với `feature_mode='simple'`: 3 features
-- ✅ Training với `feature_mode='temporal'`: 39 features
-- ✅ Prediction tự động detect mode từ metadata
-- ✅ Prediction extract đúng số features như training
-- ✅ Không còn feature mismatch errors
-
-## 📁 FILES CHANGED
-
-| File | Status | Changes |
-|------|--------|---------|
-| feature_extractor.py | ✅ NEW | Core feature extraction module |
-| train_module.py | ✅ UPDATED | Added feature_mode parameter, uses FeatureExtractor |
-| create_odc_metadata.py | ✅ UPDATED | Added feature_mode and 39 feature names |
-| model_train/model_odc_info.json | ✅ CREATED | Metadata for model_odc.joblib |
-| api_server.py | ⏳ MANUAL | Need to replace run_prediction function |
-| run_prediction_new.py | ✅ NEW | New run_prediction code using FeatureExtractor |
-| SYSTEM_UPDATE_GUIDE.md | ✅ NEW | Comprehensive guide |
-| UPDATE_SUMMARY.md | ✅ NEW | This file |
-
-## 🚀 QUICK START
-
-### Bước 1: Backup (Optional)
-```bash
-cp api_server.py api_server.py.backup
-```
-
-### Bước 2: Cập nhật api_server.py
-**Mở `api_server.py` và thay thế hàm `run_prediction`**
-
-Tìm line:
-```python
-async def run_prediction(config: PredictionConfig):
-    """Chạy prediction process - Áp dụng phương pháp từ 02.predict_ODC.ipynb"""
-```
-
-Thay thế toàn bộ hàm bằng code từ `run_prediction_new.py`
-
-### Bước 3: Restart API server
-```bash
-# Stop current server (Ctrl+C)
-# Start new server
-./start.sh
-# hoặc
-python api_server.py
-```
-
-### Bước 4: Xóa cache cũ (Optional nhưng recommended)
-```bash
-rm -rf dataset_cache/*
-```
-
-### Bước 5: Test via web interface
-1. Mở http://localhost:8000
-2. Vào Training tab
-3. Chọn feature_mode (sẽ thêm vào UI sau)
-4. Train model
-5. Vào Prediction tab
-6. Chọn model vừa train
-7. Run prediction
-
-## 🎯 TỔNG KẾT
-
-### Vấn đề ban đầu:
-- Hệ thống training và prediction không đồng bộ features
-- model_odc.joblib cần 39 features nhưng prediction chỉ tạo 3 features
-
-### Giải pháp:
-- Tạo `FeatureExtractor` module chuẩn với 3 modes
-- Cập nhật training để chọn feature mode và lưu vào metadata
-- Cập nhật prediction để đọc feature mode từ metadata và extract features tương ứng
-- Tạo metadata cho model_odc.joblib với feature_mode='temporal'
-
-### Kết quả:
-- ✅ Training và prediction hoàn toàn đồng bộ
-- ✅ Hỗ trợ 3 feature modes: simple (3), extended (15), temporal (39+)
-- ✅ Model tự động biết cần extract bao nhiêu features
-- ✅ Không còn feature mismatch errors
-- ✅ model_odc.joblib có thể sử dụng được với prediction
-
-### Lợi ích:
-1. **Linh hoạt**: Chọn feature mode phù hợp với use case
-2. **Nhất quán**: Training và prediction luôn sync
-3. **Mở rộng**: Dễ dàng thêm feature mode mới
-4. **Rõ ràng**: Metadata chứa đầy đủ thông tin về features
-5. **Tương thích**: Hỗ trợ cả model cũ và mới
-
-## 📞 SUPPORT
-
-Nếu gặp lỗi, kiểm tra:
-1. ✅ `feature_extractor.py` có trong folder chưa
-2. ✅ `api_server.py` đã cập nhật `run_prediction` chưa
-3. ✅ Model metadata có field `feature_mode` chưa
-4. ✅ Cache cũ đã xóa chưa
-
-Xem thêm: `SYSTEM_UPDATE_GUIDE.md` để biết chi tiết.

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-    <title>Batch Processing - Land Classification</title>
-    
-    <!-- Leaflet CSS -->
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-</head>
-<body>
-    <div class="container">
-        <div class="header">
-            <h1>🚀 Batch Processing</h1>
-            <p>Xử lý nhiều khu vực cùng lúc với model đã train</p>
-        </div>
-        
-        <div style="background: white; padding: 15px; display: flex; gap: 10px; flex-wrap: wrap; justify-content: center; border-bottom: 2px solid #e0e0e0;">
-            <a href="/" style="padding: 10px 20px; background: #667eea; color: white; border-radius: 8px; text-decoration: none; font-weight: 600;">🏠 Trang Chủ</a>
-            <a href="/training" style="padding: 10px 20px; background: #f093fb; color: white; border-radius: 8px; text-decoration: none; font-weight: 600;">🎓 Training</a>
-            <a href="/prediction" style="padding: 10px 20px; background: #4facfe; color: white; border-radius: 8px; text-decoration: none; font-weight: 600;">🗺️ Prediction</a>
-            <a href="/batch" style="padding: 10px 20px; background: #764ba2; color: white; border-radius: 8px; text-decoration: none; font-weight: 600;">🚀 Batch Processing (Active)</a>
-            <a href="/ndvi" style="padding: 10px 20px; background: #2ecc71; color: white; border-radius: 8px; text-decoration: none; font-weight: 600;">🌿 NDVI Analysis</a>
-            <a href="/reports" style="padding: 10px 20px; background: #ff6b6b; color: white; border-radius: 8px; text-decoration: none; font-weight: 600;">📝 Reports</a>
-        </div>
-        
-        <div class="content">
-            <!-- Configuration Section -->
-            <div class="section">
-                <h2>⚙️ Cấu hình Batch</h2>
-                
-                <div class="form-group">
-                    <label for="batchModelSelect">Model:</label>
-                    <select id="batchModelSelect">
-                        <option value="">Đang tải...</option>
-                    </select>
-                </div>
-                
-                <div class="form-group">
-                    <label for="batchName">Tên khu vực:</label>
-                    <input type="text" id="batchName" placeholder="Ví dụ: Khu vực A">
-                </div>
-                
-                <div class="form-group">
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-                </div>
-                
-                <div class="form-group">
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-                    <div style="display: grid; grid-template-columns: 1fr 1fr; gap: 10px;">
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-                        <input type="date" id="batchEndDate" value="2023-05-31">
-                    </div>
-                </div>
-                
-                <button class="btn btn-primary" onclick="addBatchItem()">
-                    ➕ Thêm vào Batch
-                </button>
-                
-                <!-- Map for selecting bbox -->
-                <div id="batchMap"></div>
-            </div>
-            
-            <!-- Batch Queue Section -->
-            <div class="section">
-                <h2>📋 Batch Queue (<span id="queueCount">0</span> items)</h2>
-                
-                <div id="batchQueue" class="jobs-list">
-                    <p style="text-align: center; color: #666;">Chưa có item nào. Thêm khu vực từ bên trái.</p>
-                </div>
-                
-                <div style="margin-top: 20px;">
-                    <button class="btn btn-success" onclick="startBatch()" id="startBatchBtn" disabled>
-                        🚀 Start Batch Processing
-                    </button>
-                    <button class="btn btn-danger" onclick="clearBatchQueue()">
-                        🗑️ Clear Queue
-                    </button>
-                </div>
-            </div>
-            
-            <!-- Status Section -->
-            <div class="section" style="grid-column: 1 / -1;">
-                <h2>📊 Batch Status</h2>
-                
-                <div id="batchStatus" style="display: none;">
-                    <div class="alert alert-info">
-                        <p><strong>Batch ID:</strong> <span id="currentBatchId"></span></p>
-                        <p><strong>Status:</strong> Queued: <span id="statusQueued">0</span> | Running: <span id="statusRunning">0</span> | Completed: <span id="statusCompleted">0</span> | Failed: <span id="statusFailed">0</span></p>
-                    </div>
-                </div>
-                
-                <div id="runningJobs" class="jobs-list">
-                    <!-- Running jobs will appear here -->
-                </div>
-            </div>
-            
-            <!-- Results Section -->
-            <div class="section" style="grid-column: 1 / -1;">
-                <h2>✅ Completed Results</h2>
-                
-                <div style="margin-bottom: 15px; display: flex; gap: 10px; align-items: center;">
-                    <button class="btn btn-primary" onclick="loadAllBatchResults()" style="padding: 8px 20px;">
-                        🔄 Refresh Results
-                    </button>
-                    <button class="btn btn-success" onclick="downloadAllResults()" style="padding: 8px 20px;">
-                        📦 Download All (Bulk)
-                    </button>
-                </div>
-                
-                <div id="completedResults" class="jobs-list">
-                    <p style="text-align: center; color: #666;">Chưa có kết quả nào</p>
-                </div>
-            </div>
-        </div>
-    </div>
-    
-    <!-- Modal for large preview -->
-    <div id="previewModal" style="display: none; position: fixed; top: 0; left: 0; width: 100%; height: 100%; background: rgba(0,0,0,0.9); z-index: 10000; padding: 20px;">
-        <div style="position: relative; height: 100%; display: flex; align-items: center; justify-content: center;">
-            <button onclick="closePreviewModal()" style="position: absolute; top: 20px; right: 20px; background: white; border: none; padding: 10px 20px; border-radius: 5px; cursor: pointer; font-size: 18px; font-weight: bold;">
-                ✕ Close
-            </button>
-            <img id="previewImage" style="max-width: 90%; max-height: 90%; border-radius: 10px;">
-        </div>
-    </div>
-    
-    <!-- Scripts -->
-    <script src="https://unpkg.com/leaflet@1.9.4/dist/leaflet.js"></script>
-    <script src="https://unpkg.com/leaflet-draw@1.0.4/dist/leaflet.draw.js"></script>
-    
-    <script>
-        let map, drawnItems, drawControl;
-        let batchQueue = [];
-        let currentBatchId = null;
-        let statusCheckInterval = null;
-        
-        // Initialize map
-        function initMap() {
-            map = L.map('batchMap').setView([9.5, 105.9], 9);
-            
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-            
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-            map.addLayer(drawnItems);
-            
-            drawControl = new L.Control.Draw({
-                draw: {
-                    rectangle: true,
-                    polygon: false,
-                    circle: false,
-                    marker: false,
-                    polyline: false,
-                    circlemarker: false
-                },
-                edit: {
-                    featureGroup: drawnItems,
-                    remove: true
-                }
-            });
-            map.addControl(drawControl);
-            
-            map.on(L.Draw.Event.CREATED, function(event) {
-                drawnItems.clearLayers();
-                const layer = event.layer;
-                drawnItems.addLayer(layer);
-                
-                const bounds = layer.getBounds();
-                document.getElementById('batchMinLon').value = bounds.getWest().toFixed(4);
-                document.getElementById('batchMinLat').value = bounds.getSouth().toFixed(4);
-                document.getElementById('batchMaxLon').value = bounds.getEast().toFixed(4);
-                document.getElementById('batchMaxLat').value = bounds.getNorth().toFixed(4);
-            });
-        }
-        
-        // Load models
-        async function loadModels() {
-            try {
-                const response = await fetch('/api/models/list');
-                const data = await response.json();
-                
-                const select = document.getElementById('batchModelSelect');
-                select.innerHTML = '<option value="">Chọn model...</option>';
-                
-                data.models.filter(m => m.filename.endsWith('.joblib')).forEach(model => {
-                    const option = document.createElement('option');
-                    option.value = model.filename;
-                    option.textContent = `${model.filename} - ${model.created}`;
-                    select.appendChild(option);
-                });
-                
-                if (data.models.length > 0) {
-                    select.value = data.models[0].filename;
-                }
-            } catch (error) {
-                console.error('Error loading models:', error);
-            }
-        }
-        
-        // Add item to batch queue
-        function addBatchItem() {
-            const name = document.getElementById('batchName').value;
-            const minLon = parseFloat(document.getElementById('batchMinLon').value);
-            const minLat = parseFloat(document.getElementById('batchMinLat').value);
-            const maxLon = parseFloat(document.getElementById('batchMaxLon').value);
-            const maxLat = parseFloat(document.getElementById('batchMaxLat').value);
-            const startDate = document.getElementById('batchStartDate').value;
-            const endDate = document.getElementById('batchEndDate').value;
-            
-            if (!name || isNaN(minLon) || isNaN(minLat) || isNaN(maxLon) || isNaN(maxLat)) {
-                alert('❌ Vui lòng điền đầy đủ thông tin!');
-                return;
-            }
-            
-            const item = {
-                name,
-                min_lon: minLon,
-                min_lat: minLat,
-                max_lon: maxLon,
-                max_lat: maxLat,
-                start_date: startDate,
-                end_date: endDate,
-                max_scenes: 12,
-                cloud_cover: 30,
-                resolution: 20
-            };
-            
-            batchQueue.push(item);
-            updateBatchQueueDisplay();
-            
-            // Clear form
-            document.getElementById('batchName').value = '';
-            drawnItems.clearLayers();
-        }
-        
-        // Update batch queue display
-        function updateBatchQueueDisplay() {
-            const queueDiv = document.getElementById('batchQueue');
-            const countSpan = document.getElementById('queueCount');
-            
-            countSpan.textContent = batchQueue.length;
-            
-            if (batchQueue.length === 0) {
-                queueDiv.innerHTML = '<p style="text-align: center; color: #666;">Chưa có item nào. Thêm khu vực từ bên trái.</p>';
-                document.getElementById('startBatchBtn').disabled = true;
-                return;
-            }
-            
-            document.getElementById('startBatchBtn').disabled = false;
-            
-            queueDiv.innerHTML = batchQueue.map((item, idx) => `
-                <div class="batch-item">
-                    <div>
-                        <strong>${item.name}</strong><br>
-                        <small>Bbox: (${item.min_lon.toFixed(2)}, ${item.min_lat.toFixed(2)}) → (${item.max_lon.toFixed(2)}, ${item.max_lat.toFixed(2)})</small><br>
-                        <small>Time: ${item.start_date} → ${item.end_date}</small>
-                    </div>
-                    <button class="btn btn-danger" style="padding: 5px 15px;" onclick="removeBatchItem(${idx})">
-                        ❌
-                    </button>
-                </div>
-            `).join('');
-        }
-        
-        // Remove item from queue
-        function removeBatchItem(index) {
-            batchQueue.splice(index, 1);
-            updateBatchQueueDisplay();
-        }
-        
-        // Clear batch queue
-        function clearBatchQueue() {
-            if (!confirm('Xóa tất cả items trong queue?')) return;
-            batchQueue = [];
-            updateBatchQueueDisplay();
-        }
-        
-        // Start batch processing
-        async function startBatch() {
-            const modelFilename = document.getElementById('batchModelSelect').value;
-            if (!modelFilename) {
-                alert('❌ Vui lòng chọn model!');
-                return;
-            }
-            
-            if (batchQueue.length === 0) {
-                alert('❌ Batch queue trống!');
-                return;
-            }
-            
-            const config = {
-                model_filename: modelFilename,
-                items: batchQueue,
-                auto_retry: true,
-                max_retries: 3
-            };
-            
-            try {
-                const response = await fetch('/api/batch/start', {
-                    method: 'POST',
-                    headers: { 'Content-Type': 'application/json' },
-                    body: JSON.stringify(config)
-                });
-                
-                const result = await response.json();
-                
-                if (response.ok) {
-                    currentBatchId = result.batch_id;
-                    document.getElementById('currentBatchId').textContent = currentBatchId;
-                    document.getElementById('batchStatus').style.display = 'block';
-                    
-                    // Clear local queue
-                    batchQueue = [];
-                    updateBatchQueueDisplay();
-                    
-                    // Start monitoring
-                    startStatusCheck();
-                    
-                    alert(`✅ Đã bắt đầu batch processing với ${result.total_jobs} jobs!`);
-                } else {
-                    throw new Error(result.detail || 'Lỗi khi bắt đầu batch');
-                }
-            } catch (error) {
-                console.error('Error starting batch:', error);
-                alert('❌ Lỗi: ' + error.message);
-            }
-        }
-        
-        // Check batch status
-        async function checkBatchStatus() {
-            try {
-                const response = await fetch('/api/batch/status');
-                const status = await response.json();
-                
-                // Update status counts
-                document.getElementById('statusQueued').textContent = status.queue.queued;
-                document.getElementById('statusRunning').textContent = status.queue.running;
-                document.getElementById('statusCompleted').textContent = status.queue.completed;
-                document.getElementById('statusFailed').textContent = status.queue.failed;
-                
-                // Update running jobs
-                const runningDiv = document.getElementById('runningJobs');
-                if (status.jobs.running.length > 0) {
-                    runningDiv.innerHTML = status.jobs.running.map(job => {
-                        const outputFile = job.result?.output_file || '';
-                        const pngFile = job.result?.png_file || '';
-                        const outputFilename = outputFile ? outputFile.split('/').pop() : '';
-                        const pngFilename = pngFile ? pngFile.split('/').pop() : '';
-                        return `
-                        <div class="batch-item running">
-                            <div style="flex: 1;">
-                                <strong>${job.name}</strong> - <span style="color: #ffc107;">Running</span><br>
-                                <small>Job ID: ${job.job_id}</small>
-                                <div class="progress">
-                                    <div class="progress-bar" style="width: ${job.progress}%">${job.progress}%</div>
-                                </div>
-                            </div>
-                            <div style="display: flex; flex-direction: column; gap: 8px; min-width: 200px; flex-shrink: 0;">
-                                <button class="btn btn-success" style="padding: 10px 20px; margin: 0; white-space: nowrap;" 
-                                        onclick="downloadResult('${outputFilename}')"
-                                        ${outputFilename ? '' : 'disabled'}>
-                                    💾 Download GeoTIFF
-                                </button>
-                                <button class="btn btn-primary" style="padding: 10px 20px; margin: 0; white-space: nowrap;" 
-                                        onclick="downloadPNG('${pngFilename}')"
-                                        ${pngFilename ? '' : 'disabled'}>
-                                    🖼️ Download PNG
-                                </button>
-                                <button class="btn btn-secondary" style="padding: 10px 20px; margin: 0; white-space: nowrap;" 
-                                        onclick="viewLargePNG('${pngFilename}')"
-                                        ${pngFilename ? '' : 'disabled'}>
-                                    🔍 View Preview
-                                </button>
-                            </div>
-                        </div>
-                        `;
-                    }).join('');
-                } else {
-                    runningDiv.innerHTML = '<p style="text-align: center; color: #666;">Không có job nào đang chạy</p>';
-                }
-                
-                // Update completed results
-                const completedDiv = document.getElementById('completedResults');
-                if (status.jobs.recent_completed.length > 0) {
-                    completedDiv.innerHTML = status.jobs.recent_completed.map(job => {
-                        const outputFile = job.result?.output_file || '';
-                        const pngFile = job.result?.png_file || '';
-                        const outputFilename = outputFile ? outputFile.split('/').pop() : '';
-                        const pngFilename = pngFile ? pngFile.split('/').pop() : '';
-                        return `
-                        <div class="batch-item completed">
-                            <div style="flex: 1;">
-                                <strong>${job.name}</strong> - <span style="color: #28a745;">✓ Completed</span><br>
-                                <small>Job ID: ${job.job_id}</small><br>
-                                <small>Completed: ${new Date(job.completed_at).toLocaleString()}</small><br>
-                                <small><strong>Shape:</strong> ${job.result?.shape ? job.result.shape.join(' x ') : 'N/A'}</small><br>
-                                <small><strong>Classes:</strong> ${job.result?.unique_classes ? job.result.unique_classes.join(', ') : 'N/A'}</small><br>
-                                <small><strong>Features:</strong> ${job.result?.n_features || 'N/A'}</small><br>
-                                <small><strong>Output:</strong> ${outputFilename || 'N/A'}</small><br>
-                                <div style="margin-top: 10px;">
-                                    <img src="/api/predictions/preview/${pngFilename}" 
-                                         style="max-width: 100%; max-height: 300px; border-radius: 5px; cursor: pointer; ${pngFilename ? '' : 'display:none;'}"
-                                         onclick="viewLargePNG('${pngFilename}')"
-                                         title="Click để xem lớn hơn"
-                                         onerror="this.style.display='none'">
-                                </div>
-                            </div>
-                            <div style="display: flex; flex-direction: column; gap: 8px; min-width: 200px; flex-shrink: 0;">
-                                <button class="btn btn-success" style="padding: 10px 20px; margin: 0; white-space: nowrap;" 
-                                        onclick="downloadResult('${outputFilename}')"
-                                        ${outputFilename ? '' : 'disabled'}>
-                                    💾 Download GeoTIFF
-                                </button>
-                                <button class="btn btn-primary" style="padding: 10px 20px; margin: 0; white-space: nowrap;" 
-                                        onclick="downloadPNG('${pngFilename}')"
-                                        ${pngFilename ? '' : 'disabled'}>
-                                    🖼️ Download PNG
-                                </button>
-                                <button class="btn btn-secondary" style="padding: 10px 20px; margin: 0; white-space: nowrap;" 
-                                        onclick="viewLargePNG('${pngFilename}')"
-                                        ${pngFilename ? '' : 'disabled'}>
-                                    🔍 View Preview
-                                </button>
-                            </div>
-                        </div>
-                        `;
-                    }).join('');
-                } else {
-                    completedDiv.innerHTML = '<p style="text-align: center; color: #666;">Chưa có kết quả nào</p>';
-                }
-                
-                // Show failed jobs if any
-                if (status.jobs.recent_failed.length > 0) {
-                    const failedHTML = status.jobs.recent_failed.map(job => {
-                        const outputFile = job.result?.output_file || '';
-                        const pngFile = job.result?.png_file || '';
-                        const outputFilename = outputFile ? outputFile.split('/').pop() : '';
-                        const pngFilename = pngFile ? pngFile.split('/').pop() : '';
-                        return `
-                        <div class="batch-item failed">
-                            <div style="flex: 1;">
-                                <strong>${job.name}</strong> - <span style="color: #dc3545;">✗ Failed</span><br>
-                                <small>Job ID: ${job.job_id}</small><br>
-                                <small style="color: #dc3545;">${job.error || 'Unknown error'}</small>
-                            </div>
-                            <div style="display: flex; flex-direction: column; gap: 8px; min-width: 200px; flex-shrink: 0;">
-                                <button class="btn btn-success" style="padding: 10px 20px; margin: 0; white-space: nowrap;" 
-                                        onclick="downloadResult('${outputFilename}')"
-                                        ${outputFilename ? '' : 'disabled'}>
-                                    💾 Download GeoTIFF
-                                </button>
-                                <button class="btn btn-primary" style="padding: 10px 20px; margin: 0; white-space: nowrap;" 
-                                        onclick="downloadPNG('${pngFilename}')"
-                                        ${pngFilename ? '' : 'disabled'}>
-                                    🖼️ Download PNG
-                                </button>
-                                <button class="btn btn-secondary" style="padding: 10px 20px; margin: 0; white-space: nowrap;" 
-                                        onclick="viewLargePNG('${pngFilename}')"
-                                        ${pngFilename ? '' : 'disabled'}>
-                                    🔍 View Preview
-                                </button>
-                            </div>
-                        </div>
-                        `;
-                    }).join('');
-                    completedDiv.innerHTML += '<h3 style="margin-top: 20px; color: #dc3545;">❌ Failed Jobs</h3>' + failedHTML;
-                }
-                
-                // Stop checking if all done
-                if (status.queue.running === 0 && status.queue.queued === 0 && currentBatchId) {
-                    stopStatusCheck();
-                    alert('✅ Batch processing hoàn thành!');
-                }
-                
-            } catch (error) {
-                console.error('Error checking batch status:', error);
-            }
-        }
-        
-        // Download result
-        function downloadResult(filename) {
-            window.location.href = `/api/predictions/download/${filename}`;
-        }
-        
-        // Download PNG
-        function downloadPNG(filename) {
-            window.location.href = `/api/predictions/preview/${filename}`;
-        }
-        
-        // View large PNG in new window
-        function viewLargePNG(filename) {
-            const modal = document.getElementById('previewModal');
-            const img = document.getElementById('previewImage');
-            img.src = `/api/predictions/preview/${filename}`;
-            modal.style.display = 'block';
-        }
-        
-        // Close preview modal
-        function closePreviewModal() {
-            document.getElementById('previewModal').style.display = 'none';
-        }
-        
-        // Load all batch results
-        async function loadAllBatchResults() {
-            try {
-                const response = await fetch('/api/batch/status');
-                const status = await response.json();
-                
-                const completedDiv = document.getElementById('completedResults');
-                
-                // Combine recent_completed from status
-                const allCompleted = status.jobs.recent_completed || [];
-                
-                if (allCompleted.length === 0) {
-                    completedDiv.innerHTML = '<p style="text-align: center; color: #666;">Chưa có kết quả nào</p>';
-                    return;
-                }
-                
-                completedDiv.innerHTML = allCompleted.map(job => `
-                    <div class="batch-item completed">
-                        <div style="flex: 1;">
-                            <strong>${job.name}</strong> - <span style="color: #28a745;">✓ Completed</span><br>
-                            <small>Job ID: ${job.job_id}</small><br>
-                            <small>Completed: ${new Date(job.completed_at).toLocaleString()}</small><br>
-                            ${job.result ? `
-                                <small><strong>Shape:</strong> ${job.result.shape.join(' x ')}</small><br>
-                                <small><strong>Classes:</strong> ${job.result.unique_classes.join(', ')}</small><br>
-                                <small><strong>Features:</strong> ${job.result.n_features}</small><br>
-                                <small><strong>Model:</strong> ${job.result.model_used}</small><br>
-                                ${job.result.png_file ? `
-                                    <div style="margin-top: 10px;">
-                                        <img src="/api/predictions/preview/${job.result.png_file.split('/').pop()}" 
-                                             style="max-width: 100%; border-radius: 5px; cursor: pointer; box-shadow: 0 2px 8px rgba(0,0,0,0.2);"
-                                             onclick="viewLargePNG('${job.result.png_file.split('/').pop()}')"
-                                             title="Click để xem lớn hơn">
-                                    </div>
-                                ` : ''}
-                            ` : ''}
-                        </div>
-                        <div style="display: flex; flex-direction: column; gap: 5px; min-width: 200px;">
-                            ${job.result && job.result.output_file ? `
-                                <button class="btn btn-success" style="padding: 8px 20px;" onclick="downloadResult('${job.result.output_file.split('/').pop()}')">
-                                    💾 Download GeoTIFF
-                                </button>
-                                ${job.result.png_file ? `
-                                    <button class="btn btn-primary" style="padding: 8px 20px;" onclick="downloadPNG('${job.result.png_file.split('/').pop()}')">
-                                        🖼️ Download PNG
-                                    </button>
-                                    <button class="btn btn-secondary" style="padding: 8px 20px;" onclick="viewLargePNG('${job.result.png_file.split('/').pop()}')">
-                                        🔍 View Preview
-                                    </button>
-                                ` : ''}
-                            ` : ''}
-                        </div>
-                    </div>
-                `).join('');
-                
-            } catch (error) {
-                console.error('Error loading batch results:', error);
-            }
-        }
-        
-        // Download all results as ZIP (placeholder)
-        function downloadAllResults() {
-            alert('💡 Tính năng download tất cả batch results sẽ được thêm trong phiên bản tiếp theo.\\nHiện tại vui lòng download từng file riêng lẻ.');
-        }
-        
-        // Start/stop status monitoring
-        function startStatusCheck() {
-            if (statusCheckInterval) clearInterval(statusCheckInterval);
-            statusCheckInterval = setInterval(checkBatchStatus, 3000);
-        }
-        
-        function stopStatusCheck() {
-            if (statusCheckInterval) {
-                clearInterval(statusCheckInterval);
-                statusCheckInterval = null;
-            }
-        }
-        
-        // Initialize on load
-        window.onload = function() {
-            initMap();
-            loadModels();
-        };
-        
-        // Cleanup on unload
-        window.onbeforeunload = function() {
-            stopStatusCheck();
-        };
-    </script>
-</body>
-</html>

change_detection_interface.html

@@ -1,383 +0,0 @@
-<!DOCTYPE html>
-<html lang="vi">
-<head>
-    <meta charset="UTF-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <title>Change Detection - Compare Current vs Future Land Use</title>
-    
-    <!-- Leaflet CSS -->
-    <link rel="stylesheet" href="https://unpkg.com/leaflet@1.9.4/dist/leaflet.css" />
-    
-    <style>
-        * { margin: 0; padding: 0; box-sizing: border-box; }
-        body { font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif; background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); min-height: 100vh; padding: 20px; }
-        .container { max-width: 1400px; margin: 0 auto; background: white; border-radius: 12px; box-shadow: 0 20px 60px rgba(0,0,0,0.3); overflow: hidden; }
-        .header { background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); color: white; padding: 30px; text-align: center; }
-        .header h1 { font-size: 32px; margin-bottom: 10px; }
-        .header p { font-size: 16px; opacity: 0.9; }
-        .content { padding: 30px; display: grid; grid-template-columns: 1fr 1fr; gap: 30px; }
-        .left-panel, .right-panel { display: flex; flex-direction: column; gap: 20px; }
-        #map { width: 100%; height: 400px; border-radius: 8px; border: 2px solid #e0e0e0; }
-        .section { background: #f8f9fa; padding: 20px; border-radius: 8px; border-left: 4px solid #667eea; }
-        .section h2 { color: #333; font-size: 18px; margin-bottom: 15px; display: flex; align-items: center; gap: 8px; }
-        .form-group { margin-bottom: 15px; }
-        .form-group label { display: block; margin-bottom: 6px; color: #555; font-weight: 500; font-size: 14px; }
-        .form-group input[type="text"], .form-group input[type="date"], .form-group input[type="number"], .form-group select { width: 100%; padding: 10px 12px; border: 1px solid #ddd; border-radius: 6px; font-size: 14px; font-family: inherit; transition: all 0.3s ease; }
-        .form-group input:focus, .form-group select:focus { outline: none; border-color: #667eea; box-shadow: 0 0 0 3px rgba(102, 126, 234, 0.1); }
-        .form-row { display: grid; grid-template-columns: 1fr 1fr; gap: 15px; }
-        .bbox-display { background: white; padding: 12px; border-radius: 6px; font-size: 13px; color: #666; font-family: monospace; border: 1px dashed #667eea; word-break: break-all; }
-        .btn { padding: 12px 24px; border: none; border-radius: 6px; font-size: 14px; font-weight: 600; cursor: pointer; transition: all 0.3s ease; display: flex; align-items: center; justify-content: center; gap: 8px; width: 100%; }
-        .btn-primary { background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); color: white; }
-        .btn-primary:hover { transform: translateY(-2px); box-shadow: 0 10px 20px rgba(102, 126, 234, 0.3); }
-        .btn:disabled { opacity: 0.5; cursor: not-allowed; transform: none; }
-        .result { background: white; border: 2px solid #e0e0e0; border-radius: 8px; padding: 20px; display: none; animation: slideIn 0.3s ease; max-height: 600px; overflow-y: auto; }
-        .result.success { border-color: #4caf50; background: #f1f8f5; }
-        .result.error { border-color: #f44336; background: #fdf5f4; }
-        .result.processing { border-color: #2196f3; background: #f3f8fd; }
-        .result h3 { margin-bottom: 15px; color: #333; }
-        .result table { width: 100%; border-collapse: collapse; margin: 15px 0; }
-        .result table th, .result table td { padding: 10px; text-align: left; border-bottom: 1px solid #e0e0e0; }
-        .result table th { background: #f0f0f0; font-weight: 600; color: #333; }
-        .result pre { background: #f5f5f5; padding: 15px; border-radius: 6px; overflow-x: auto; font-size: 12px; color: #333; max-height: 300px; overflow-y: auto; border-left: 4px solid #667eea; }
-        .error-text { color: #f44336; font-weight: 500; }
-        .success-text { color: #4caf50; font-weight: 500; }
-        .processing-text { color: #2196f3; font-weight: 500; }
-        .progress { width: 100%; height: 6px; background: #e0e0e0; border-radius: 3px; overflow: hidden; margin: 10px 0; }
-        .progress-bar { height: 100%; background: linear-gradient(90deg, #667eea 0%, #764ba2 100%); width: 0%; transition: width 0.3s ease; }
-        .stat-box { background: white; padding: 15px; border-radius: 6px; border-left: 4px solid #667eea; margin: 10px 0; }
-        .stat-label { font-size: 12px; color: #999; text-transform: uppercase; margin-bottom: 5px; }
-        .stat-value { font-size: 20px; font-weight: 600; color: #333; }
-        .info-box { background: #e3f2fd; padding: 12px; border-radius: 6px; border-left: 4px solid #2196f3; font-size: 13px; color: #1565c0; }
-        @keyframes slideIn { from { opacity: 0; transform: translateY(-10px); } to { opacity: 1; transform: translateY(0); } }
-        @media (max-width: 1024px) { .content { grid-template-columns: 1fr; } }
-    </style>
-</head>
-<body>
-    <div class="container">
-        <div class="header">
-            <h1>🔍 Change Detection - Land Use Analysis</h1>
-            <p>Compare current land use with predicted future changes</p>
-        </div>
-        
-        <div class="content">
-            <!-- Left Panel -->
-            <div class="left-panel">
-                <div class="section">
-                    <h2><span>🗺️</span>Select Area on Map</h2>
-                    <p style="color: #999; font-size: 13px; margin-bottom: 10px;">Click on map to select bounding box</p>
-                    <div id="map"></div>
-                    <div class="form-group" style="margin-top: 10px;">
-                        <label>BBox (min_lon, min_lat, max_lon, max_lat)</label>
-                        <div class="bbox-display" id="bboxDisplay">Click on map to select area</div>
-                    </div>
-                </div>
-                
-                <div class="section">
-                    <h2><span>📅</span>Current Period (Baseline)</h2>
-                    <div class="form-row">
-                        <div class="form-group">
-                            <label>Start Date</label>
-                            <input type="date" id="currentStartDate" value="2022-01-01">
-                        </div>
-                        <div class="form-group">
-                            <label>End Date</label>
-                            <input type="date" id="currentEndDate" value="2022-03-31">
-                        </div>
-                    </div>
-                </div>
-                
-                <div class="section">
-                    <h2><span>🔮</span>Prediction Period (Future)</h2>
-                    <div class="form-row">
-                        <div class="form-group">
-                            <label>Start Date</label>
-                            <input type="date" id="predictionStartDate" value="2023-01-01">
-                        </div>
-                        <div class="form-group">
-                            <label>End Date</label>
-                            <input type="date" id="predictionEndDate" value="2023-03-31">
-                        </div>
-                    </div>
-                </div>
-                
-                <div class="section">
-                    <h2><span>⚙️</span>Parameters</h2>
-                    <div class="form-row">
-                        <div class="form-group">
-                            <label>Max Scenes</label>
-                            <input type="number" id="maxScenes" value="12" min="1" max="100">
-                        </div>
-                        <div class="form-group">
-                            <label>Cloud Cover %</label>
-                            <input type="number" id="cloudCover" value="30" min="0" max="100">
-                        </div>
-                    </div>
-                    
-                    <div class="form-group">
-                        <label>Resolution (m)</label>
-                        <input type="number" id="resolution" value="20" min="10" max="100" step="10">
-                    </div>
-                </div>
-            </div>
-            
-            <!-- Right Panel -->
-            <div class="right-panel">
-                <div class="section">
-                    <h2><span>🤖</span>Select Trained Model</h2>
-                    <div class="form-group">
-                        <label>Trained Model</label>
-                        <select id="modelSelect">
-                            <option value="">Loading models...</option>
-                        </select>
-                    </div>
-                    <div id="modelInfo" style="font-size: 12px; color: #999; margin-top: 10px;"></div>
-                </div>
-                
-                <div class="section">
-                    <h2><span>�</span>Workflow</h2>
-                    <div class="info-box">
-                        1️⃣ Classify current period satellite data<br>
-                        2️⃣ Classify future period satellite data<br>
-                        3️⃣ Compare to detect land use changes
-                    </div>
-                </div>
-                
-                <div class="section">
-                    <button class="btn btn-primary" id="runBtn" onclick="runChangeDetection()" disabled>
-                        <span>▶️</span>Compare Periods
-                    </button>
-                </div>
-                
-                <div id="resultDiv" class="result"></div>
-            </div>
-        </div>
-    </div>
-    
-    <script src="https://unpkg.com/leaflet@1.9.4/dist/leaflet.js"></script>
-    <script>
-        const API_BASE = 'http://localhost:8000/api';
-        let map, rectangle;
-        let bbox = null;
-        
-        function initMap() {
-            map = L.map('map').setView([9.8, 105.85], 10);
-            L.tileLayer('https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png', {
-                maxZoom: 19,
-                attribution: '© OpenStreetMap contributors'
-            }).addTo(map);
-            
-            const defaultBbox = [105.6, 9.3, 106.2, 9.8];
-            drawBboxRectangle(defaultBbox);
-            
-            map.on('click', function(e) {
-                const size = 0.3;
-                const bounds = L.latLngBounds([
-                    [e.latlng.lat - size, e.latlng.lng - size],
-                    [e.latlng.lat + size, e.latlng.lng + size]
-                ]);
-                drawBboxRectangle([bounds.getWest(), bounds.getSouth(), bounds.getEast(), bounds.getNorth()]);
-            });
-        }
-        
-        function drawBboxRectangle(bboxArray) {
-            const [minLon, minLat, maxLon, maxLat] = bboxArray;
-            if (rectangle) map.removeLayer(rectangle);
-            
-            rectangle = L.rectangle([[minLat, minLon], [maxLat, maxLon]], {
-                color: '#667eea', weight: 2, fillColor: '#667eea', fillOpacity: 0.1
-            }).addTo(map);
-            
-            map.fitBounds(rectangle.getBounds());
-            bbox = bboxArray;
-            document.getElementById('bboxDisplay').textContent = 
-                `[${minLon.toFixed(4)}, ${minLat.toFixed(4)}, ${maxLon.toFixed(4)}, ${maxLat.toFixed(4)}]`;
-            updateRunButtonState();
-        }
-        
-        async function loadModels() {
-            try {
-                const response = await fetch(`${API_BASE}/models/list`);
-                const data = await response.json();
-                
-                const modelSelect = document.getElementById('modelSelect');
-                modelSelect.innerHTML = '<option value="">-- Select a model --</option>';
-                
-                if (data.models && data.models.length > 0) {
-                    data.models.forEach(model => {
-                        const option = document.createElement('option');
-                        option.value = model.filename;
-                        option.textContent = `${model.filename} (${model.size_mb}MB)`;
-                        modelSelect.appendChild(option);
-                    });
-                } else {
-                    modelSelect.innerHTML = '<option value="">No trained models found</option>';
-                }
-                
-                modelSelect.addEventListener('change', () => {
-                    updateModelInfo();
-                    updateRunButtonState();
-                });
-            } catch (error) {
-                console.error('Error loading models:', error);
-                document.getElementById('modelSelect').innerHTML = '<option value="">Error loading models</option>';
-            }
-        }
-        
-        function updateModelInfo() {
-            const modelName = document.getElementById('modelSelect').value;
-            document.getElementById('modelInfo').textContent = modelName ? `Selected: ${modelName}` : '';
-        }
-        
-        function updateRunButtonState() {
-            const runBtn = document.getElementById('runBtn');
-            runBtn.disabled = !bbox || !document.getElementById('modelSelect').value;
-        }
-        
-        async function runChangeDetection() {
-            const resultDiv = document.getElementById('resultDiv');
-            const runBtn = document.getElementById('runBtn');
-            
-            if (!bbox) {
-                showResult('error', 'Error', 'Please select an area on the map');
-                return;
-            }
-            
-            const modelFilename = document.getElementById('modelSelect').value;
-            if (!modelFilename) {
-                showResult('error', 'Error', 'Please select a trained model');
-                return;
-            }
-            
-            runBtn.disabled = true;
-            showResult('processing', 'Processing', 'Analyzing land use changes...');
-            
-            try {
-                const [minLon, minLat, maxLon, maxLat] = bbox;
-                const currentStartDate = document.getElementById('currentStartDate').value;
-                const currentEndDate = document.getElementById('currentEndDate').value;
-                const predictionStartDate = document.getElementById('predictionStartDate').value;
-                const predictionEndDate = document.getElementById('predictionEndDate').value;
-                const maxScenes = parseInt(document.getElementById('maxScenes').value);
-                const cloudCover = parseInt(document.getElementById('cloudCover').value);
-                const resolution = parseInt(document.getElementById('resolution').value);
-                
-                showResult('processing', 'Step 1/3', 'Classifying current period (baseline)...');
-                
-                const payload = {
-                    model_filename: modelFilename,
-                    min_lon: minLon, min_lat: minLat, max_lon: maxLon, max_lat: maxLat,
-                    current_period: {
-                        start_date: currentStartDate,
-                        end_date: currentEndDate
-                    },
-                    prediction_period: {
-                        start_date: predictionStartDate,
-                        end_date: predictionEndDate
-                    },
-                    max_scenes: maxScenes,
-                    cloud_cover: cloudCover,
-                    resolution: resolution,
-                    export_ndvi: true,
-                    export_classification: true
-                };
-                
-                const changeResponse = await fetch(`${API_BASE}/change-detection/compare-periods`, {
-                    method: 'POST',
-                    headers: {'Content-Type': 'application/json'},
-                    body: JSON.stringify(payload)
-                });
-                
-                if (!changeResponse.ok) {
-                    const errorData = await changeResponse.json();
-                    throw new Error(errorData.detail || 'Analysis failed');
-                }
-                
-                const changeResult = await changeResponse.json();
-                displayResults(changeResult);
-                
-            } catch (error) {
-                console.error('Error:', error);
-                showResult('error', 'Error', error.message);
-            } finally {
-                runBtn.disabled = false;
-            }
-        }
-        
-        function displayResults(result) {
-            const resultDiv = document.getElementById('resultDiv');
-            let html = '<h3 class="success-text">✓ Change Detection Completed</h3>';
-            
-            // Current period classification
-            if (result.current_classification) {
-                const curr = result.current_classification;
-                html += '<div class="stat-box"><div class="stat-label">📊 Current Period Classification</div>';
-                html += `<div style="color: #666; font-size: 12px; margin-bottom: 10px;">Scenes: ${curr.n_scenes} | Resolution: ${curr.resolution}m</div>`;
-                
-                if (curr.class_distribution) {
-                    html += '<table>';
-                    Object.entries(curr.class_distribution).forEach(([cls, count]) => {
-                        const percentage = ((count / Object.values(curr.class_distribution).reduce((a,b) => a+b, 0)) * 100).toFixed(1);
-                        html += `<tr><td>Class ${cls}:</td><td><strong>${count}</strong> (${percentage}%)</td></tr>`;
-                    });
-                    html += '</table>';
-                }
-                html += '</div>';
-            }
-            
-            // Prediction period classification
-            if (result.prediction_classification) {
-                const pred = result.prediction_classification;
-                html += '<div class="stat-box"><div class="stat-label">🔮 Prediction Period Classification</div>';
-                html += `<div style="color: #666; font-size: 12px; margin-bottom: 10px;">Scenes: ${pred.n_scenes} | Resolution: ${pred.resolution}m</div>`;
-                
-                if (pred.class_distribution) {
-                    html += '<table>';
-                    Object.entries(pred.class_distribution).forEach(([cls, count]) => {
-                        const percentage = ((count / Object.values(pred.class_distribution).reduce((a,b) => a+b, 0)) * 100).toFixed(1);
-                        html += `<tr><td>Class ${cls}:</td><td><strong>${count}</strong> (${percentage}%)</td></tr>`;
-                    });
-                    html += '</table>';
-                }
-                html += '</div>';
-            }
-            
-            // Change detection
-            if (result.change_detection) {
-                const cd = result.change_detection;
-                html += '<div class="stat-box"><div class="stat-label">🔄 Change Detection Summary</div>';
-                html += `<div class="stat-value" style="color: #e74c3c;">${(cd.change_rate * 100).toFixed(2)}% Changed</div>`;
-                html += '<table>';
-                html += '<tr><td>Changed Pixels:</td><td><strong>' + cd.n_changed_pixels.toLocaleString() + '</strong></td></tr>';
-                html += '<tr><td>Total Pixels:</td><td><strong>' + cd.n_total_pixels.toLocaleString() + '</strong></td></tr>';
-                html += '</table>';
-                
-                if (Object.keys(cd.change_matrix).length > 0) {
-                    html += '<div style="margin-top: 10px;"><strong>Transitions (Current → Prediction):</strong></div>';
-                    html += '<pre>' + JSON.stringify(cd.change_matrix, null, 2) + '</pre>';
-                }
-                html += '</div>';
-            }
-            
-            resultDiv.innerHTML = html;
-            resultDiv.className = 'result success';
-            resultDiv.style.display = 'block';
-        }
-        
-        function showResult(type, title, message) {
-            const resultDiv = document.getElementById('resultDiv');
-            const typeClass = type === 'error' ? 'error' : (type === 'processing' ? 'processing' : 'success');
-            const textClass = type === 'error' ? 'error-text' : (type === 'processing' ? 'processing-text' : 'success-text');
-            
-            resultDiv.innerHTML = `<h3 class="${textClass}">${title}</h3><p>${message}</p>` +
-                (type === 'processing' ? '<div class="progress"><div class="progress-bar" style="animation: progress 2s infinite;"></div></div>' : '');
-            resultDiv.className = `result ${typeClass}`;
-            resultDiv.style.display = 'block';
-        }
-        
-        document.addEventListener('DOMContentLoaded', () => {
-            initMap();
-            loadModels();
-        });
-    </script>
-</body>
-</html>

check_versions.py

@@ -1,5 +0,0 @@
-import xarray as xr
-import rasterio
-
-print(f"xarray version: {xr.__version__}")
-print(f"rasterio version: {rasterio.__version__}")

cloud_removal.py

@@ -1,628 +0,0 @@
-"""
-Cloud Removal Module - Hệ thống xử lý mây độc lập
-Cung cấp nhiều phương pháp khử mây cho dữ liệu Sentinel-2
-"""
-
-import numpy as np
-import xarray as xr
-from typing import Tuple, Optional, Dict
-from sklearn.neighbors import KNeighborsRegressor
-from sklearn.ensemble import RandomForestRegressor
-import warnings
-warnings.filterwarnings('ignore')
-
-
-class CloudRemovalStrategy:
-    """Base class cho các chiến lược xử lý mây"""
-    
-    def __init__(self, name: str, description: str):
-        self.name = name
-        self.description = description
-    
-    def remove_clouds(self, s2_data: xr.Dataset, cloud_mask: xr.DataArray) -> Tuple[xr.Dataset, Dict]:
-        """
-        Xử lý mây và trả về dữ liệu đã được làm sạch
-        
-        Returns:
-            Tuple[xr.Dataset, Dict]: (cleaned_data, metadata)
-        """
-        raise NotImplementedError
-
-
-class ClassicStrategy(CloudRemovalStrategy):
-    """
-    Chiến lược cổ điển 3 bước:
-    1. Temporal interpolation (ffill + bfill)
-    2. Median compositing (nếu >= 3 scenes)
-    3. Spatial interpolation (nearest neighbor)
-    """
-    
-    def __init__(self):
-        super().__init__(
-            name="classic",
-            description="3-step classical approach: temporal → median → spatial interpolation"
-        )
-    
-    def remove_clouds(self, s2_data: xr.Dataset, cloud_mask: xr.DataArray) -> Tuple[xr.Dataset, Dict]:
-        metadata = {
-            'method': self.name,
-            'steps_applied': []
-        }
-        
-        # Apply mask
-        for band in s2_data.data_vars:
-            if band != "SCL":
-                s2_data[band] = s2_data[band].where(~cloud_mask)
-        
-        # Step 1: Temporal Interpolation
-        for band in s2_data.data_vars:
-            if band != "SCL":
-                s2_data[band] = s2_data[band].ffill(dim='time').bfill(dim='time')
-        metadata['steps_applied'].append('temporal_interpolation')
-        
-        # Step 2: Median Compositing (if >= 3 time steps)
-        if len(s2_data.time) >= 3:
-            for band in s2_data.data_vars:
-                if band != "SCL":
-                    median_composite = s2_data[band].median(dim='time', skipna=True)
-                    s2_data[band] = s2_data[band].fillna(median_composite)
-            metadata['steps_applied'].append('median_compositing')
-        
-        # Step 3: Spatial Interpolation
-        for band in s2_data.data_vars:
-            if band != "SCL":
-                s2_data[band] = s2_data[band].interpolate_na(dim='x', method='nearest', fill_value='extrapolate')
-                s2_data[band] = s2_data[band].interpolate_na(dim='y', method='nearest', fill_value='extrapolate')
-        metadata['steps_applied'].append('spatial_interpolation')
-        
-        # Final fallback
-        for band in s2_data.data_vars:
-            if band != "SCL":
-                s2_data[band] = s2_data[band].fillna(0)
-        
-        return s2_data, metadata
-
-
-class NoRemovalStrategy(CloudRemovalStrategy):
-    """Không xử lý mây - giữ nguyên dữ liệu gốc, chỉ fill NaN bằng 0"""
-    
-    def __init__(self):
-        super().__init__(
-            name="none",
-            description="No cloud removal - keep original data with NaN filled as 0"
-        )
-    
-    def remove_clouds(self, s2_data: xr.Dataset, cloud_mask: xr.DataArray) -> Tuple[xr.Dataset, Dict]:
-        metadata = {
-            'method': self.name,
-            'steps_applied': ['none'],
-            'note': 'No cloud removal applied, only NaN filling'
-        }
-        
-        # Chỉ fill NaN bằng 0, không apply cloud mask
-        for band in s2_data.data_vars:
-            if band != "SCL":
-                s2_data[band] = s2_data[band].fillna(0)
-        
-        return s2_data, metadata
-
-
-class TemporalOnlyStrategy(CloudRemovalStrategy):
-    """Chỉ sử dụng temporal interpolation - nhanh nhất, phù hợp khi có nhiều time steps"""
-    
-    def __init__(self):
-        super().__init__(
-            name="temporal_only",
-            description="Temporal interpolation only - fast, good for time series with many scenes"
-        )
-    
-    def remove_clouds(self, s2_data: xr.Dataset, cloud_mask: xr.DataArray) -> Tuple[xr.Dataset, Dict]:
-        metadata = {
-            'method': self.name,
-            'steps_applied': ['temporal_interpolation']
-        }
-        
-        # Apply mask
-        for band in s2_data.data_vars:
-            if band != "SCL":
-                s2_data[band] = s2_data[band].where(~cloud_mask)
-        
-        # Temporal interpolation
-        for band in s2_data.data_vars:
-            if band != "SCL":
-                s2_data[band] = s2_data[band].ffill(dim='time').bfill(dim='time')
-                s2_data[band] = s2_data[band].fillna(0)
-        
-        return s2_data, metadata
-
-
-class MedianCompositeStrategy(CloudRemovalStrategy):
-    """Ưu tiên median composite - tốt nhất cho giảm noise"""
-    
-    def __init__(self):
-        super().__init__(
-            name="median_composite",
-            description="Median composite priority - best for noise reduction"
-        )
-    
-    def remove_clouds(self, s2_data: xr.Dataset, cloud_mask: xr.DataArray) -> Tuple[xr.Dataset, Dict]:
-        metadata = {
-            'method': self.name,
-            'steps_applied': ['median_compositing', 'spatial_interpolation']
-        }
-        
-        # Apply mask
-        for band in s2_data.data_vars:
-            if band != "SCL":
-                s2_data[band] = s2_data[band].where(~cloud_mask)
-        
-        # Direct median composite
-        for band in s2_data.data_vars:
-            if band != "SCL":
-                median_composite = s2_data[band].median(dim='time', skipna=True)
-                # Fill all NaN with median
-                s2_data[band] = s2_data[band].fillna(median_composite)
-        
-        # Spatial interpolation for remaining gaps
-        for band in s2_data.data_vars:
-            if band != "SCL":
-                s2_data[band] = s2_data[band].interpolate_na(dim='x', method='nearest')
-                s2_data[band] = s2_data[band].interpolate_na(dim='y', method='nearest')
-                s2_data[band] = s2_data[band].fillna(0)
-        
-        return s2_data, metadata
-
-
-class MLInpaintingStrategy(CloudRemovalStrategy):
-    """
-    Machine Learning Inpainting - sử dụng KNN hoặc Random Forest
-    Học từ pixels hợp lệ để dự đoán pixels bị mây
-    """
-    
-    def __init__(self, ml_model: str = "knn"):
-        """
-        Args:
-            ml_model: 'knn' hoặc 'rf' (random forest)
-        """
-        super().__init__(
-            name=f"ml_inpainting_{ml_model}",
-            description=f"ML-based cloud removal using {ml_model.upper()} - learns from valid pixels"
-        )
-        self.ml_model = ml_model
-    
-    def remove_clouds(self, s2_data: xr.Dataset, cloud_mask: xr.DataArray) -> Tuple[xr.Dataset, Dict]:
-        metadata = {
-            'method': self.name,
-            'ml_model': self.ml_model,
-            'steps_applied': []
-        }
-        
-        # Apply mask
-        for band in s2_data.data_vars:
-            if band != "SCL":
-                s2_data[band] = s2_data[band].where(~cloud_mask)
-        
-        # ML inpainting cho từng time step
-        for time_idx in range(len(s2_data.time)):
-            # Get all bands for this time step
-            bands_data = []
-            band_names = []
-            
-            for band in s2_data.data_vars:
-                if band != "SCL":
-                    band_data = s2_data[band].isel(time=time_idx).values
-                    bands_data.append(band_data.flatten())
-                    band_names.append(band)
-            
-            if not bands_data:
-                continue
-            
-            # Stack bands: shape (n_pixels, n_bands)
-            X_all = np.column_stack(bands_data)
-            
-            # Find valid (non-NaN) and invalid (NaN) pixels
-            valid_mask = ~np.isnan(X_all).any(axis=1)
-            
-            if valid_mask.sum() < 10:  # Not enough training data
-                continue
-            
-            X_valid = X_all[valid_mask]
-            X_invalid_indices = np.where(~valid_mask)[0]
-            
-            if len(X_invalid_indices) == 0:  # No clouds
-                continue
-            
-            # Prepare features: use spatial coordinates + spectral values
-            y_coords, x_coords = np.meshgrid(
-                np.arange(s2_data.dims['y']),
-                np.arange(s2_data.dims['x']),
-                indexing='ij'
-            )
-            coords_flat = np.column_stack([y_coords.flatten(), x_coords.flatten()])
-            
-            # Train ML model on valid pixels
-            X_train = coords_flat[valid_mask]
-            y_train = X_valid
-            
-            try:
-                if self.ml_model == "knn":
-                    model = KNeighborsRegressor(n_neighbors=min(5, len(X_train)), weights='distance')
-                else:  # random forest
-                    model = RandomForestRegressor(n_estimators=10, max_depth=10, random_state=42, n_jobs=-1)
-                
-                model.fit(X_train, y_train)
-                
-                # Predict invalid pixels
-                X_test = coords_flat[X_invalid_indices]
-                predictions = model.predict(X_test)
-                
-                # Fill predictions back
-                X_all[X_invalid_indices] = predictions
-                
-                # Reshape and update dataset
-                for band_idx, band in enumerate(band_names):
-                    filled_data = X_all[:, band_idx].reshape(s2_data.dims['y'], s2_data.dims['x'])
-                    s2_data[band].values[time_idx] = filled_data
-                
-                metadata['steps_applied'].append(f'ml_inpainting_time_{time_idx}')
-                
-            except Exception as e:
-                print(f"[ML INPAINTING] Error at time {time_idx}: {e}")
-                continue
-        
-        # Final cleanup
-        for band in s2_data.data_vars:
-            if band != "SCL":
-                s2_data[band] = s2_data[band].fillna(0)
-        
-        return s2_data, metadata
-
-
-class DeepInpaintingStrategy(CloudRemovalStrategy):
-    """
-    Deep Learning Inpainting - sử dụng U-Net CNN
-    Phức tạp hơn nhưng cho kết quả tốt nhất với large cloud gaps
-    
-    Note: Yêu cầu pretrained model (train bằng train_cloud_removal.py)
-    """
-    
-    def __init__(self, model_path: Optional[str] = None):
-        super().__init__(
-            name="deep_inpainting",
-            description="Deep Learning U-Net based cloud removal - best quality for large gaps"
-        )
-        self.model_path = model_path or "model_train/cloud_removal_unet_best.pth"
-        self.model = None
-        self.device = None
-        
-        # Try to load model if provided
-        if model_path or Path(self.model_path).exists():
-            try:
-                import torch
-                import torch.nn as nn
-                
-                # Load checkpoint
-                checkpoint = torch.load(self.model_path, map_location='cpu')
-                
-                # Recreate U-Net architecture
-                from train_cloud_removal import UNet
-                self.model = UNet(
-                    in_channels=checkpoint.get('in_channels', 4),
-                    out_channels=checkpoint.get('out_channels', 4)
-                )
-                self.model.load_state_dict(checkpoint['model_state_dict'])
-                
-                # Set device
-                self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-                self.model = self.model.to(self.device)
-                self.model.eval()
-                
-                print(f"[DEEP INPAINTING] Loaded U-Net model from {self.model_path}")
-                print(f"[DEEP INPAINTING] Using device: {self.device}")
-            except Exception as e:
-                print(f"[DEEP INPAINTING] Could not load model: {e}")
-                self.model = None
-    
-    def remove_clouds(self, s2_data: xr.Dataset, cloud_mask: xr.DataArray) -> Tuple[xr.Dataset, Dict]:
-        metadata = {
-            'method': self.name,
-            'has_model': self.model is not None,
-            'steps_applied': []
-        }
-        
-        # Apply mask
-        for band in s2_data.data_vars:
-            if band != "SCL":
-                s2_data[band] = s2_data[band].where(~cloud_mask)
-        
-        if self.model is None:
-            # Fallback to classical method
-            print("[DEEP INPAINTING] No model available, falling back to median composite")
-            for band in s2_data.data_vars:
-                if band != "SCL":
-                    median_composite = s2_data[band].median(dim='time', skipna=True)
-                    s2_data[band] = s2_data[band].fillna(median_composite)
-                    s2_data[band] = s2_data[band].interpolate_na(dim='x', method='nearest')
-                    s2_data[band] = s2_data[band].interpolate_na(dim='y', method='nearest')
-                    s2_data[band] = s2_data[band].fillna(0)
-            metadata['steps_applied'].append('fallback_median')
-        else:
-            # Use U-Net for cloud removal
-            print("[DEEP INPAINTING] Applying U-Net cloud removal...")
-            import torch
-            
-            try:
-                # Process each time step
-                for time_idx in range(len(s2_data.time)):
-                    # Get bands for this time step (B02, B03, B04, B08)
-                    bands_to_process = ['B02', 'B03', 'B04', 'B08']
-                    available_bands = [b for b in bands_to_process if b in s2_data.data_vars]
-                    
-                    if len(available_bands) < 4:
-                        print(f"[DEEP INPAINTING] Warning: Not all required bands available, skipping time {time_idx}")
-                        continue
-                    
-                    # Stack bands [C, H, W]
-                    input_bands = []
-                    for band in available_bands:
-                        band_data = s2_data[band].isel(time=time_idx).values.astype(np.float32)
-                        # Normalize to [0, 1] (S2 values are typically 0-10000)
-                        band_data = np.clip(band_data / 10000.0, 0, 1)
-                        input_bands.append(band_data)
-                    
-                    input_array = np.stack(input_bands, axis=0)  # [C, H, W]
-                    
-                    # Convert to tensor and add batch dimension
-                    input_tensor = torch.from_numpy(input_array).unsqueeze(0).to(self.device)
-                    
-                    # Run through U-Net
-                    with torch.no_grad():
-                        output_tensor = self.model(input_tensor)
-                    
-                    # Convert back to numpy
-                    output_array = output_tensor[0].cpu().numpy()  # [C, H, W]
-                    
-                    # Denormalize back to original scale
-                    output_array = output_array * 10000.0
-                    
-                    # Update dataset with cleaned data
-                    for i, band in enumerate(available_bands):
-                        s2_data[band].values[time_idx] = output_array[i]
-                    
-                    metadata['steps_applied'].append(f'unet_time_{time_idx}')
-                
-                print(f"[DEEP INPAINTING] Processed {len(s2_data.time)} time steps with U-Net")
-                
-            except Exception as e:
-                print(f"[DEEP INPAINTING] Error during inference: {e}")
-                # Fallback to classical method
-                for band in s2_data.data_vars:
-                    if band != "SCL":
-                        s2_data[band] = s2_data[band].ffill(dim='time').bfill(dim='time')
-                        s2_data[band] = s2_data[band].fillna(0)
-                metadata['steps_applied'].append('unet_error_fallback')
-        
-        return s2_data, metadata
-
-
-class HybridStrategy(CloudRemovalStrategy):
-    """
-    Hybrid Strategy - kết hợp Classical + ML
-    1. Classical temporal interpolation (nhanh)
-    2. ML inpainting cho gaps còn lại (chất lượng cao)
-    3. Spatial interpolation (cleanup)
-    """
-    
-    def __init__(self):
-        super().__init__(
-            name="hybrid",
-            description="Hybrid classical + ML - balanced speed and quality"
-        )
-    
-    def remove_clouds(self, s2_data: xr.Dataset, cloud_mask: xr.DataArray) -> Tuple[xr.Dataset, Dict]:
-        metadata = {
-            'method': self.name,
-            'steps_applied': []
-        }
-        
-        # Apply mask
-        for band in s2_data.data_vars:
-            if band != "SCL":
-                s2_data[band] = s2_data[band].where(~cloud_mask)
-        
-        # Step 1: Temporal interpolation (fast)
-        for band in s2_data.data_vars:
-            if band != "SCL":
-                s2_data[band] = s2_data[band].ffill(dim='time').bfill(dim='time')
-        metadata['steps_applied'].append('temporal_interpolation')
-        
-        # Step 2: Check remaining NaN percentage
-        nan_count = 0
-        total_count = 0
-        for band in s2_data.data_vars:
-            if band != "SCL":
-                nan_count += np.isnan(s2_data[band].values).sum()
-                total_count += s2_data[band].values.size
-        
-        nan_percentage = (nan_count / total_count * 100) if total_count > 0 else 0
-        
-        # Step 3: ML inpainting if still significant gaps (>5%)
-        if nan_percentage > 5.0:
-            print(f"[HYBRID] {nan_percentage:.1f}% NaN remaining, applying ML inpainting...")
-            ml_strategy = MLInpaintingStrategy(ml_model="knn")
-            s2_data, ml_meta = ml_strategy.remove_clouds(s2_data, cloud_mask)
-            metadata['steps_applied'].extend(['ml_inpainting_knn'])
-            metadata['nan_before_ml'] = nan_percentage
-        else:
-            # Step 4: Spatial interpolation for small gaps
-            for band in s2_data.data_vars:
-                if band != "SCL":
-                    s2_data[band] = s2_data[band].interpolate_na(dim='x', method='nearest')
-                    s2_data[band] = s2_data[band].interpolate_na(dim='y', method='nearest')
-            metadata['steps_applied'].append('spatial_interpolation')
-        
-        # Final cleanup
-        for band in s2_data.data_vars:
-            if band != "SCL":
-                s2_data[band] = s2_data[band].fillna(0)
-        
-        return s2_data, metadata
-
-
-# ============ FACTORY & UTILITIES ============
-
-def get_available_methods() -> Dict[str, str]:
-    """Trả về dictionary của tất cả methods có sẵn"""
-    return {
-        "none": "No cloud removal - keep original data (fastest, may have cloud artifacts)",
-        "classic": "3-step classical: temporal → median → spatial (default, balanced)",
-        "temporal_only": "Temporal interpolation only (fast, needs many scenes)",
-        "median_composite": "Median composite priority (best noise reduction)",
-        "ml_knn": "ML K-Nearest Neighbors inpainting (good quality, medium speed)",
-        "ml_rf": "ML Random Forest inpainting (high quality, slower)",
-        "deep": "Deep Learning CNN inpainting (best quality, requires model)",
-        "hybrid": "Hybrid classical + ML (balanced speed & quality)"
-    }
-
-
-def create_cloud_removal_strategy(method: str = "classic", **kwargs) -> CloudRemovalStrategy:
-    """
-    Factory function để tạo strategy từ tên method
-    
-    Args:
-        method: Tên method ("classic", "temporal_only", "median_composite", 
-                "ml_knn", "ml_rf", "deep", "hybrid")
-        **kwargs: Additional parameters cho specific strategies
-    
-    Returns:
-        CloudRemovalStrategy instance
-    """
-    method = method.lower()
-    
-    if method == "none":
-        return NoRemovalStrategy()
-    elif method == "classic":
-        return ClassicStrategy()
-    elif method == "temporal_only":
-        return TemporalOnlyStrategy()
-    elif method == "median_composite":
-        return MedianCompositeStrategy()
-    elif method == "ml_knn":
-        return MLInpaintingStrategy(ml_model="knn")
-    elif method == "ml_rf":
-        return MLInpaintingStrategy(ml_model="rf")
-    elif method == "deep":
-        model_path = kwargs.get('model_path', None)
-        return DeepInpaintingStrategy(model_path=model_path)
-    elif method == "hybrid":
-        return HybridStrategy()
-    else:
-        print(f"[CLOUD REMOVAL] Unknown method '{method}', using 'classic'")
-        return ClassicStrategy()
-
-
-def process_cloud_removal(
-    s2_data: xr.Dataset,
-    method: str = "classic",
-    verbose: bool = True,
-    **kwargs
-) -> Tuple[xr.Dataset, Dict]:
-    """
-    Main entry point cho cloud removal
-    
-    Args:
-        s2_data: Sentinel-2 dataset với SCL band
-        method: Cloud removal method name
-        verbose: Print progress messages
-        **kwargs: Additional parameters
-    
-    Returns:
-        Tuple[xr.Dataset, Dict]: (cleaned_data, metadata)
-    """
-    if verbose:
-        print(f"[CLOUD REMOVAL] Using method: {method}")
-    
-    # Detect clouds from SCL
-    if "SCL" not in s2_data:
-        if verbose:
-            print("[CLOUD REMOVAL] Warning: No SCL band, cannot mask clouds")
-        return s2_data, {'method': 'none', 'warning': 'no_scl_band'}
-    
-    scl = s2_data["SCL"]
-    
-    # Create comprehensive cloud mask
-    cloud_mask = (scl == 3) | (scl == 8) | (scl == 9) | (scl == 10) | (scl == 11)
-    invalid_mask = (scl == 0) | (scl == 1)
-    full_mask = cloud_mask | invalid_mask
-    
-    # Calculate coverage
-    total_pixels = full_mask.size
-    masked_pixels = int(full_mask.sum().values)
-    cloud_coverage_percent = (masked_pixels / total_pixels * 100) if total_pixels > 0 else 0
-    
-    if verbose:
-        print(f"[CLOUD REMOVAL] Cloud coverage: {cloud_coverage_percent:.1f}%")
-        print(f"[CLOUD REMOVAL] Masked pixels: {masked_pixels:,}/{total_pixels:,}")
-    
-    # Create strategy and process
-    strategy = create_cloud_removal_strategy(method, **kwargs)
-    cleaned_data, metadata = strategy.remove_clouds(s2_data.copy(deep=True), full_mask)
-    
-    # Add coverage info to metadata
-    metadata['cloud_coverage_percent'] = float(cloud_coverage_percent)
-    metadata['masked_pixels'] = masked_pixels
-    metadata['total_pixels'] = total_pixels
-    
-    if verbose:
-        print(f"[CLOUD REMOVAL] Completed using {metadata['method']}")
-        print(f"[CLOUD REMOVAL] Steps: {', '.join(metadata['steps_applied'])}")
-    
-    return cleaned_data, metadata
-
-
-# ============ TESTING & COMPARISON ============
-
-def compare_methods(s2_data: xr.Dataset, methods: list = None) -> Dict:
-    """
-    So sánh các methods khác nhau trên cùng dữ liệu
-    
-    Args:
-        s2_data: Sentinel-2 dataset
-        methods: List of method names to compare (default: all)
-    
-    Returns:
-        Dict: Comparison results
-    """
-    if methods is None:
-        methods = ["classic", "temporal_only", "median_composite", "ml_knn", "hybrid"]
-    
-    results = {}
-    
-    for method in methods:
-        try:
-            print(f"\n{'='*60}")
-            print(f"Testing: {method}")
-            print(f"{'='*60}")
-            
-            cleaned_data, metadata = process_cloud_removal(s2_data, method=method, verbose=True)
-            
-            # Calculate remaining NaN
-            nan_count = sum(np.isnan(cleaned_data[band].values).sum() 
-                          for band in cleaned_data.data_vars if band != "SCL")
-            total_count = sum(cleaned_data[band].values.size 
-                            for band in cleaned_data.data_vars if band != "SCL")
-            
-            results[method] = {
-                'metadata': metadata,
-                'remaining_nan_percent': (nan_count / total_count * 100) if total_count > 0 else 0,
-                'success': True
-            }
-            
-        except Exception as e:
-            results[method] = {
-                'error': str(e),
-                'success': False
-            }
-            print(f"[ERROR] {method}: {e}")
-    
-    return results

cloud_removal_train.ipynb

@@ -1,10 +0,0 @@
-{
- "cells": [],
- "metadata": {
-  "language_info": {
-   "name": "python"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

cloud_training_interface.html

@@ -1,796 +0,0 @@
-<!DOCTYPE html>
-<html lang="vi">
-<head>
-    <meta charset="UTF-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <title>Cloud Removal Training - Deep Learning</title>
-    <style>
-        * {
-            margin: 0;
-            padding: 0;
-            box-sizing: border-box;
-        }
-
-        body {
-            font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', 'Roboto', 'Oxygen', 'Ubuntu', 'Cantarell', sans-serif;
-            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
-            background-attachment: fixed;
-            min-height: 100vh;
-            padding: 20px;
-        }
-
-        .container {
-            max-width: 1200px;
-            margin: 0 auto;
-            background: rgba(255, 255, 255, 0.95);
-            backdrop-filter: blur(20px);
-            border-radius: 24px;
-            box-shadow: 0 25px 80px rgba(0,0,0,0.2), 0 0 0 1px rgba(255,255,255,0.1);
-            overflow: hidden;
-        }
-
-        .header {
-            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
-            color: white;
-            padding: 40px 30px;
-            text-align: center;
-            position: relative;
-            overflow: hidden;
-        }
-        
-        .header::before {
-            content: '';
-            position: absolute;
-            top: -50%;
-            right: -50%;
-            width: 200%;
-            height: 200%;
-            background: radial-gradient(circle, rgba(255,255,255,0.1) 0%, transparent 70%);
-            animation: headerGlow 8s ease-in-out infinite;
-        }
-        
-        @keyframes headerGlow {
-            0%, 100% { transform: translate(0, 0); }
-            50% { transform: translate(-20%, -20%); }
-        }
-
-        .header h1 {
-            font-size: 2.8em;
-            margin-bottom: 12px;
-            font-weight: 700;
-            position: relative;
-            z-index: 1;
-            text-shadow: 0 2px 20px rgba(0,0,0,0.2);
-        }
-
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-            font-size: 1.15em;
-            opacity: 0.95;
-            position: relative;
-            z-index: 1;
-            font-weight: 400;
-        }
-
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-            background: rgba(255,255,255,0.8);
-            backdrop-filter: blur(10px);
-            padding: 18px 30px;
-            border-bottom: 1px solid rgba(0,0,0,0.08);
-            box-shadow: 0 2px 10px rgba(0,0,0,0.03);
-            display: flex;
-            gap: 12px;
-            flex-wrap: wrap;
-            justify-content: center;
-        }
-
-        .nav a {
-            padding: 12px 24px;
-            color: white;
-            text-decoration: none;
-            border-radius: 12px;
-            font-weight: 600;
-            transition: all 0.3s;
-            box-shadow: 0 4px 12px rgba(102, 126, 234, 0.2);
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-        
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-
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-
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-            margin-bottom: 30px;
-            padding: 28px;
-            background: linear-gradient(135deg, #f8f9fa 0%, #ffffff 100%);
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-            border: 1px solid rgba(0,0,0,0.06);
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-            transition: all 0.3s ease;
-        }
-        
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-            transform: translateY(-2px);
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-            gap: 10px;
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-            background: linear-gradient(135deg, #f8f9fa 0%, #ffffff 100%);
-            border-radius: 12px;
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-            margin-bottom: 20px;
-            border: 1px solid rgba(0,0,0,0.05);
-            box-shadow: 0 2px 8px rgba(0,0,0,0.04);
-        }
-
-        .form-group {
-            margin-bottom: 20px;
-            position: relative;
-        }
-
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-            display: block;
-            font-weight: 600;
-            margin-bottom: 8px;
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-            font-size: 1em;
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-            background: linear-gradient(135deg, #dc3545 0%, #c82333 100%);
-            color: white;
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-            transform: translateY(-3px);
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-            color: #6b7280;
-            margin: 6px 0;
-            line-height: 1.5;
-        }
-
-        .status-badge {
-            display: inline-block;
-            padding: 6px 16px;
-            border-radius: 20px;
-            font-size: 0.85em;
-            font-weight: 600;
-            margin-top: 12px;
-        }
-
-        .status-success {
-            background: linear-gradient(135deg, #d4edda 0%, #c3e6cb 100%);
-            color: #155724;
-            box-shadow: 0 2px 8px rgba(21, 87, 36, 0.2);
-        }
-
-        .status-training {
-            background: linear-gradient(135deg, #fff3cd 0%, #ffeaa7 100%);
-            color: #856404;
-            box-shadow: 0 2px 8px rgba(133, 100, 4, 0.2);
-        }
-
-        .status-error {
-            background: linear-gradient(135deg, #f8d7da 0%, #f5c6cb 100%);
-            color: #721c24;
-            box-shadow: 0 2px 8px rgba(114, 28, 36, 0.2);
-        }
-
-        .progress-bar {
-            width: 100%;
-            height: 32px;
-            background: linear-gradient(to right, #e5e7eb, #f3f4f6);
-            border-radius: 16px;
-            overflow: hidden;
-            margin: 20px 0;
-            box-shadow: inset 0 2px 8px rgba(0,0,0,0.08);
-            border: 1px solid rgba(0,0,0,0.05);
-        }
-
-        .progress-fill {
-            height: 100%;
-            background: linear-gradient(90deg, #667eea 0%, #764ba2 50%, #667eea 100%);
-            background-size: 200% 100%;
-            animation: shimmer 2s infinite;
-            transition: width 0.3s;
-            display: flex;
-            align-items: center;
-            justify-content: center;
-            color: white;
-            font-weight: 700;
-            font-size: 0.9em;
-            box-shadow: 0 2px 8px rgba(102, 126, 234, 0.4);
-        }
-        
-        @keyframes shimmer {
-            0% { background-position: 200% 0; }
-            100% { background-position: -200% 0; }
-        }
-
-        .info-box {
-            background: linear-gradient(135deg, #e3f2fd 0%, #f0f7ff 100%);
-            border-left: 5px solid #2196F3;
-            padding: 20px;
-            border-radius: 12px;
-            margin-bottom: 20px;
-            box-shadow: 0 4px 15px rgba(33, 150, 243, 0.1);
-            transition: all 0.3s ease;
-        }
-        
-        .info-box:hover {
-            box-shadow: 0 6px 25px rgba(33, 150, 243, 0.15);
-            transform: translateX(3px);
-        }
-
-        .warning-box {
-            background: linear-gradient(135deg, #fff3cd 0%, #ffeaa7 100%);
-            border-left: 5px solid #ffc107;
-            padding: 20px;
-            border-radius: 12px;
-            margin-bottom: 20px;
-            box-shadow: 0 4px 15px rgba(255, 193, 7, 0.1);
-            transition: all 0.3s ease;
-        }
-        
-        .warning-box:hover {
-            box-shadow: 0 6px 25px rgba(255, 193, 7, 0.15);
-            transform: translateX(3px);
-        }
-
-        .grid-2 {
-            display: grid;
-            grid-template-columns: 1fr 1fr;
-            gap: 20px;
-        }
-
-        @media (max-width: 768px) {
-            .grid-2 {
-                grid-template-columns: 1fr;
-            }
-
-            .model-list {
-                grid-template-columns: 1fr;
-            }
-        }
-
-        .logs {
-            background: #1e1e1e;
-            color: #d4d4d4;
-            padding: 20px;
-            border-radius: 12px;
-            font-family: 'Courier New', monospace;
-            font-size: 0.9em;
-            max-height: 400px;
-            overflow-y: auto;
-            margin-top: 20px;
-            box-shadow: inset 0 2px 10px rgba(0,0,0,0.3);
-        }
-
-        .logs .log-entry {
-            margin: 5px 0;
-            padding: 4px 0;
-        }
-
-        .logs .log-info {
-            color: #4ec9b0;
-        }
-
-        .logs .log-warning {
-            color: #dcdcaa;
-        }
-
-        .logs .log-error {
-            color: #f48771;
-        }
-    </style>
-</head>
-<body>
-    <div class="container">
-        <div class="header">
-            <h1>🌥️ Cloud Removal Training</h1>
-            <p>Train Deep Learning Models để khử mây từ ảnh Sentinel-2</p>
-        </div>
-
-        <div class="nav">
-            <a href="/">← Trang chủ</a>
-            <a href="/training">Land Classification</a>
-            <a href="/prediction">Prediction</a>
-            <a href="#models">Models đã train</a>
-        </div>
-
-        <div class="content">
-            <!-- Info Section -->
-            <div class="section">
-                <div class="info-box">
-                    <strong>📚 Dataset:</strong> SEN12MS-CR (Sentinel-12 Multi-Seasonal Cloud Removal)<br>
-                    <strong>🏗️ Architecture:</strong> U-Net với skip connections<br>
-                    <strong>📊 Input:</strong> S2 cloudy (4 bands) + S1 radar (2 bands) = 6 channels<br>
-                    <strong>🎯 Output:</strong> S2 clean (4 bands)<br>
-                    <strong>⏱️ Training time:</strong> ~2-3 hours (GPU) / ~20-30 hours (CPU)
-                </div>
-            </div>
-
-            <!-- Training Configuration -->
-            <div class="section">
-                <h2 class="section-title">⚙️ Cấu hình Training</h2>
-                
-                <div class="card">
-                    <form id="trainingForm">
-                        <div class="grid-2">
-                            <div class="form-group">
-                                <label>🏗️ Model Architecture</label>
-                                <select id="modelArchitecture" required>
-                                    <option value="unet">U-Net (Classic CNN)</option>
-                                    <option value="crgan">CR-GAN (Cloud Removal GAN)</option>
-                                    <option value="spagan">SpA-GAN (Spatial Attention GAN)</option>
-                                    <option value="glfcr">GLF-CR (Global-Local Fusion)</option>
-                                    <option value="sen12mscr">SEN12MS-CR (Multi-modal)</option>
-                                    <option value="rsdehazenet">RSDehazeNet (Remote Sensing)</option>
-                                    <option value="cloudnet">Cloud-Net (Encoder-Decoder)</option>
-                                    <option value="dsen2cr">DSen2-CR (Deep Sentinel-2)</option>
-                                </select>
-                                <small style="color: #6c757d;">Chọn kiến trúc deep learning cho cloud removal</small>
-                            </div>
-
-                            <div class="form-group">
-                                <label>🏷️ Model Name</label>
-                                <input type="text" id="modelName" value="cloud_removal_unet" required>
-                                <small style="color: #6c757d;">Tên model để lưu</small>
-                            </div>
-
-                            <div class="form-group">
-                                <label>📂 Data Directory</label>
-                                <input type="text" id="dataDir" value="winter_dataset" required>
-                                <small style="color: #6c757d;">Thư mục chứa dữ liệu SEN12MS-CR</small>
-                            </div>
-
-                            <div class="form-group">
-                                <label>📦 Batch Size</label>
-                                <input type="number" id="batchSize" value="8" min="1" max="32" required>
-                                <small style="color: #6c757d;">Giảm xuống 4 hoặc 2 nếu GPU hết RAM</small>
-                            </div>
-
-                            <div class="form-group">
-                                <label>🔄 Number of Epochs</label>
-                                <input type="number" id="numEpochs" value="50" min="1" max="200" required>
-                                <small style="color: #6c757d;">Số lượng epochs training</small>
-                            </div>
-
-                            <div class="form-group">
-                                <label>📈 Learning Rate</label>
-                                <input type="number" id="learningRate" value="0.0001" step="0.00001" min="0.00001" max="0.01" required>
-                                <small style="color: #6c757d;">Learning rate (default: 1e-4)</small>
-                            </div>
-
-                            <div class="form-group">
-                                <div class="checkbox-group">
-                                    <input type="checkbox" id="useS1" checked>
-                                    <label for="useS1">📡 Use Sentinel-1 (Radar Data)</label>
-                                </div>
-                                <small style="color: #6c757d;">Sử dụng dữ liệu radar (VV, VH) để cải thiện kết quả</small>
-                            </div>
-
-                            <div class="form-group">
-                                <div class="checkbox-group">
-                                    <input type="checkbox" id="useGPU" checked>
-                                    <label for="useGPU">🚀 Use GPU</label>
-                                </div>
-                                <small style="color: #6c757d;">Sử dụng GPU để training nhanh hơn</small>
-                            </div>
-                        </div>
-
-                        <div class="form-group" style="margin-top: 20px;">
-                            <button type="submit" class="btn btn-primary">🚀 Start Training</button>
-                            <button type="button" class="btn btn-secondary" onclick="refreshModels()">🔄 Refresh Models</button>
-                        </div>
-                    </form>
-                </div>
-            </div>
-
-            <!-- Training Status -->
-            <div class="section" id="trainingStatus" style="display: none;">
-                <h2 class="section-title">📊 Training Status</h2>
-                <div class="card">
-                    <div id="statusMessage"></div>
-                    <div class="progress-bar">
-                        <div class="progress-fill" id="progressBar" style="width: 0%;">0%</div>
-                    </div>
-                    <div class="logs" id="trainingLogs">
-                        <div class="log-entry log-info">Training logs will appear here...</div>
-                    </div>
-                </div>
-            </div>
-
-            <!-- Models List -->
-            <div class="section" id="models">
-                <h2 class="section-title">🤖 Cloud Removal Models</h2>
-                <div class="model-list" id="modelsList">
-                    <div class="model-card">
-                        <p style="text-align: center; color: #6c757d;">Loading models...</p>
-                    </div>
-                </div>
-            </div>
-
-            <!-- Methods Info -->
-            <div class="section">
-                <h2 class="section-title">📖 Cloud Removal Deep Learning Architectures</h2>
-                <div class="grid-2">
-                    <div class="card">
-                        <h3>🔹 U-Net</h3>
-                        <p>Classic encoder-decoder with skip connections. Fast training, good baseline performance.</p>
-                        <div class="status-badge status-success">Recommended for beginners</div>
-                    </div>
-
-                    <div class="card">
-                        <h3>🔹 CR-GAN</h3>
-                        <p>Cloud Removal GAN - adversarial training cho kết quả chân thực hơn.</p>
-                        <div class="status-badge status-training">Advanced</div>
-                    </div>
-
-                    <div class="card">
-                        <h3>🔹 SpA-GAN</h3>
-                        <p>Spatial Attention GAN - attention mechanism tập trung vào vùng có mây.</p>
-                        <div class="status-badge status-success">Best quality</div>
-                    </div>
-
-                    <div class="card">
-                        <h3>🔹 GLF-CR</h3>
-                        <p>Global-Local Fusion - kết hợp features global và local cho chi tiết tốt hơn.</p>
-                        <div class="status-badge status-training">High accuracy</div>
-                    </div>
-
-                    <div class="card">
-                        <h3>🔹 SEN12MS-CR</h3>
-                        <p>Multi-modal fusion - kết hợp Sentinel-1 radar và Sentinel-2 optical.</p>
-                        <div class="status-badge status-success">Multi-sensor</div>
-                    </div>
-
-                    <div class="card">
-                        <h3>🔹 RSDehazeNet</h3>
-                        <p>Remote Sensing Dehaze Network - chuyên cho ảnh viễn thám.</p>
-                        <div class="status-badge status-training">RS specialized</div>
-                    </div>
-
-                    <div class="card">
-                        <h3>🔹 Cloud-Net</h3>
-                        <p>Encoder-Decoder architecture với residual connections.</p>
-                        <div class="status-badge status-success">Balanced</div>
-                    </div>
-
-                    <div class="card">
-                        <h3>🔹 DSen2-CR</h3>
-                        <p>Deep Sentinel-2 Cloud Removal - tận dụng temporal information.</p>
-                        <div class="status-badge status-training">Temporal fusion</div>
-                    </div>
-                </div>
-            </div>
-        </div>
-    </div>
-
-    <script>
-        // Load models on page load
-        window.addEventListener('load', () => {
-            refreshModels();
-            loadCloudRemovalMethods();
-        });
-
-        // Handle training form submission
-        document.getElementById('trainingForm').addEventListener('submit', async (e) => {
-            e.preventDefault();
-
-            const config = {
-                data_dir: document.getElementById('dataDir').value,
-                model_name: document.getElementById('modelName').value,
-                architecture: document.getElementById('modelArchitecture').value,
-                use_s1: document.getElementById('useS1').checked,
-                batch_size: parseInt(document.getElementById('batchSize').value),
-                num_epochs: parseInt(document.getElementById('numEpochs').value),
-                learning_rate: parseFloat(document.getElementById('learningRate').value),
-                use_gpu: document.getElementById('useGPU').checked
-            };
-
-            try {
-                const response = await fetch('/api/cloud-removal/train', {
-                    method: 'POST',
-                    headers: { 'Content-Type': 'application/json' },
-                    body: JSON.stringify(config)
-                });
-
-                const result = await response.json();
-
-                if (response.ok) {
-                    // Show training status section
-                    document.getElementById('trainingStatus').style.display = 'block';
-                    document.getElementById('statusMessage').innerHTML = `
-                        <div class="status-badge status-training">Training Started: ${result.training_id}</div>
-                        <p style="margin-top: 10px;">Model training has started in background. This may take several hours.</p>
-                    `;
-
-                    addLog('info', `Training started: ${result.training_id}`);
-                    addLog('info', `Config: ${JSON.stringify(config, null, 2)}`);
-
-                    // Simulate progress (actual progress would come from websocket)
-                    simulateProgress();
-                } else {
-                    alert('Error starting training: ' + (result.detail || result.error));
-                }
-            } catch (error) {
-                alert('Error: ' + error.message);
-            }
-        });
-
-        // Refresh models list
-        async function refreshModels() {
-            try {
-                const response = await fetch('/api/cloud-removal/models');
-                const data = await response.json();
-
-                const modelsList = document.getElementById('modelsList');
-
-                if (data.models && data.models.length > 0) {
-                    modelsList.innerHTML = data.models.map(model => `
-                        <div class="model-card">
-                            <h3>📦 ${model.filename}</h3>
-                            <div class="model-info">🏗️ Architecture: ${model.architecture || 'U-Net'}</div>
-                            <div class="model-info">📊 Epoch: ${model.epoch}</div>
-                            <div class="model-info">📉 Train Loss: ${model.train_loss.toFixed(6)}</div>
-                            <div class="model-info">📉 Val Loss: ${model.val_loss.toFixed(6)}</div>
-                            <div class="model-info">📡 Use S1: ${model.use_s1 ? 'Yes' : 'No'}</div>
-                            <div class="model-info">💾 Size: ${model.size_mb.toFixed(2)} MB</div>
-                            <div class="model-info">📅 Created: ${new Date(model.created * 1000).toLocaleString()}</div>
-                            <div style="margin-top: 15px;">
-                                <button class="btn btn-danger" onclick="deleteModel('${model.filename}')">
-                                    🗑️ Delete
-                                </button>
-                            </div>
-                        </div>
-                    `).join('');
-                } else {
-                    modelsList.innerHTML = `
-                        <div class="model-card">
-                            <p style="text-align: center; color: #6c757d;">
-                                No cloud removal models found.<br>
-                                Train your first model above!
-                            </p>
-                        </div>
-                    `;
-                }
-            } catch (error) {
-                console.error('Error loading models:', error);
-            }
-        }
-
-        // Delete model
-        async function deleteModel(filename) {
-            if (!confirm(`Delete model ${filename}?`)) return;
-
-            try {
-                const response = await fetch(`/api/cloud-removal/models/${filename}`, {
-                    method: 'DELETE'
-                });
-
-                if (response.ok) {
-                    alert('Model deleted successfully');
-                    refreshModels();
-                } else {
-                    const error = await response.json();
-                    alert('Error deleting model: ' + error.detail);
-                }
-            } catch (error) {
-                alert('Error: ' + error.message);
-            }
-        }
-
-        // Load cloud removal methods
-        async function loadCloudRemovalMethods() {
-            try {
-                const response = await fetch('/api/cloud-removal/methods');
-                const data = await response.json();
-                console.log('Available cloud removal methods:', data.methods);
-            } catch (error) {
-                console.error('Error loading methods:', error);
-            }
-        }
-
-        // Add log entry
-        function addLog(type, message) {
-            const logs = document.getElementById('trainingLogs');
-            const timestamp = new Date().toLocaleTimeString();
-            const logClass = type === 'error' ? 'log-error' : (type === 'warning' ? 'log-warning' : 'log-info');
-            
-            const entry = document.createElement('div');
-            entry.className = `log-entry ${logClass}`;
-            entry.textContent = `[${timestamp}] ${message}`;
-            
-            logs.appendChild(entry);
-            logs.scrollTop = logs.scrollHeight;
-        }
-
-        // Simulate progress (replace with real progress tracking)
-        function simulateProgress() {
-            let progress = 0;
-            const interval = setInterval(() => {
-                progress += Math.random() * 5;
-                if (progress >= 100) {
-                    progress = 100;
-                    clearInterval(interval);
-                    addLog('info', 'Training completed! Check models list below.');
-                    setTimeout(refreshModels, 2000);
-                }
-                
-                const progressBar = document.getElementById('progressBar');
-                progressBar.style.width = progress + '%';
-                progressBar.textContent = Math.floor(progress) + '%';
-
-                if (progress % 10 < 5) {
-                    addLog('info', `Training progress: ${Math.floor(progress)}%`);
-                }
-            }, 3000);
-        }
-    </script>
-</body>
-</html>

create_odc_metadata.py

@@ -1,81 +0,0 @@
-"""
-Tạo metadata cho model_odc.joblib (legacy model)
-"""
-
-import json
-from pathlib import Path
-
-# Metadata cho model_odc.joblib  
-# Model này là GridSearchCV Pipeline với 39 features (temporal mode)
-# Features: NDVI time series + NDWI time series + NDBI time series + radar features
-
-# Calculate feature names for temporal mode with 12 timesteps
-# (12 NDVI + 12 NDWI + 12 NDBI + 3 radar = 39 features)
-n_timesteps = 12
-feature_names = []
-
-# NDVI time series
-for t in range(n_timesteps):
-    feature_names.append(f"NDVI_t{t+1}")
-
-# NDWI time series
-for t in range(n_timesteps):
-    feature_names.append(f"NDWI_t{t+1}")
-
-# NDBI time series
-for t in range(n_timesteps):
-    feature_names.append(f"NDBI_t{t+1}")
-
-# Radar features
-feature_names.extend(["VH_db_mean", "VV_db_mean", "VH_VV_ratio"])
-
-metadata = {
-    "timestamp": "2025-12-20T10:00:00",
-    "data_source": "Unknown (Legacy model)",
-    "collections": ["sentinel-2-l2a", "sentinel-1-rtc"],
-    "features": feature_names,
-    "feature_mode": "temporal",  # IMPORTANT: temporal mode with 39 features
-    "training_samples": None,
-    "testing_samples": None,
-    "test_size": 0.2,
-    "train_accuracy": None,
-    "test_accuracy": None,
-    "model_type": "random_forest",  # GridSearchCV with RandomForest
-    "device": "cpu",
-    "n_estimators": 100,
-    "max_depth": None,
-    "learning_rate": None,
-    "cnn_epochs": None,
-    "n_features": 39,  # GridSearchCV expects 39 features!
-    "n_classes": 8,
-    "class_names": [
-        "Lua tom",  # 0
-        "Lua",      # 1
-        "CHN",      # 2
-        "CLN",      # 3
-        "TS",       # 4
-        "Song",     # 5
-        "Dat xay dung",  # 6
-        "Rung"      # 7
-    ],
-    "classification_report": None,
-    "confusion_matrix": None,
-    "bbox": None,
-    "time_range": None,
-    "resolution": 10,
-    "notes": "Legacy GridSearchCV Pipeline model with 39 temporal features (12 timesteps each for NDVI/NDWI/NDBI + 3 radar features). Requires temporal mode feature extraction."
-}
-
-# Save metadata
-model_train_dir = Path("model_train")
-metadata_file = model_train_dir / "model_odc_info.json"
-
-print("Creating metadata for model_odc.joblib...")
-print(f"Saving to: {metadata_file}")
-
-with open(metadata_file, 'w') as f:
-    json.dump(metadata, f, indent=2)
-
-print("✅ Metadata created successfully!")
-print("\nMetadata content:")
-print(json.dumps(metadata, indent=2))

dashboard.html

@@ -1,935 +0,0 @@
-<!DOCTYPE html>
-<html lang="vi">
-<head>
-    <meta charset="UTF-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <title>Dashboard - Land Classification System</title>
-    <style>
-        * {
-            margin: 0;
-            padding: 0;
-            box-sizing: border-box;
-        }
-
-        body {
-            font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, sans-serif;
-            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
-            min-height: 100vh;
-            padding: 20px;
-        }
-
-        .container {
-            max-width: 1400px;
-            margin: 0 auto;
-        }
-
-        .header {
-            background: white;
-            padding: 25px;
-            border-radius: 15px;
-            box-shadow: 0 10px 30px rgba(0, 0, 0, 0.1);
-            margin-bottom: 30px;
-            text-align: center;
-        }
-
-        .header h1 {
-            color: #667eea;
-            font-size: 2.5em;
-            margin-bottom: 10px;
-        }
-
-        .header p {
-            color: #666;
-            font-size: 1.1em;
-        }
-
-        .nav-tabs {
-            display: flex;
-            gap: 10px;
-            margin-bottom: 20px;
-            background: white;
-            padding: 15px;
-            border-radius: 15px;
-            box-shadow: 0 5px 15px rgba(0, 0, 0, 0.1);
-        }
-
-        .nav-tab {
-            flex: 1;
-            padding: 15px 25px;
-            background: #f5f5f5;
-            border: none;
-            border-radius: 10px;
-            cursor: pointer;
-            font-size: 1.1em;
-            font-weight: 600;
-            transition: all 0.3s;
-            color: #666;
-        }
-
-        .nav-tab:hover {
-            background: #e0e0e0;
-        }
-
-        .nav-tab.active {
-            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
-            color: white;
-            box-shadow: 0 5px 15px rgba(102, 126, 234, 0.4);
-        }
-
-        .tab-content {
-            display: none;
-        }
-
-        .tab-content.active {
-            display: block;
-        }
-
-        .stats-grid {
-            display: grid;
-            grid-template-columns: repeat(auto-fit, minmax(280px, 1fr));
-            gap: 20px;
-            margin-bottom: 30px;
-        }
-
-        .stat-card {
-            background: white;
-            padding: 25px;
-            border-radius: 15px;
-            box-shadow: 0 10px 30px rgba(0, 0, 0, 0.1);
-            transition: transform 0.3s;
-        }
-
-        .stat-card:hover {
-            transform: translateY(-5px);
-        }
-
-        .stat-card .icon {
-            font-size: 3em;
-            margin-bottom: 15px;
-        }
-
-        .stat-card .value {
-            font-size: 2.5em;
-            font-weight: bold;
-            color: #667eea;
-            margin-bottom: 5px;
-        }
-
-        .stat-card .label {
-            color: #666;
-            font-size: 1.1em;
-        }
-
-        .chart-container {
-            background: white;
-            padding: 30px;
-            border-radius: 15px;
-            box-shadow: 0 10px 30px rgba(0, 0, 0, 0.1);
-            margin-bottom: 30px;
-        }
-
-        .chart-container h3 {
-            margin-bottom: 20px;
-            color: #333;
-            font-size: 1.5em;
-        }
-
-        .chart-wrapper {
-            position: relative;
-            height: 400px;
-        }
-
-        canvas {
-            max-height: 100%;
-        }
-
-        .batch-queue {
-            background: white;
-            padding: 30px;
-            border-radius: 15px;
-            box-shadow: 0 10px 30px rgba(0, 0, 0, 0.1);
-        }
-
-        .batch-item {
-            padding: 20px;
-            border: 2px solid #e0e0e0;
-            border-radius: 10px;
-            margin-bottom: 15px;
-            transition: all 0.3s;
-        }
-
-        .batch-item:hover {
-            border-color: #667eea;
-            box-shadow: 0 5px 15px rgba(102, 126, 234, 0.2);
-        }
-
-        .batch-item.running {
-            border-color: #4caf50;
-            background: #f1f8f4;
-        }
-
-        .batch-item.completed {
-            border-color: #2196f3;
-            background: #e3f2fd;
-        }
-
-        .batch-item.failed {
-            border-color: #f44336;
-            background: #ffebee;
-        }
-
-        .batch-header {
-            display: flex;
-            justify-content: space-between;
-            align-items: center;
-            margin-bottom: 10px;
-        }
-
-        .batch-name {
-            font-size: 1.2em;
-            font-weight: 600;
-            color: #333;
-        }
-
-        .batch-status {
-            padding: 8px 16px;
-            border-radius: 20px;
-            font-weight: 600;
-            font-size: 0.9em;
-        }
-
-        .batch-status.queued {
-            background: #fff3cd;
-            color: #856404;
-        }
-
-        .batch-status.running {
-            background: #d4edda;
-            color: #155724;
-        }
-
-        .batch-status.completed {
-            background: #cce5ff;
-            color: #004085;
-        }
-
-        .batch-status.failed {
-            background: #f8d7da;
-            color: #721c24;
-        }
-
-        .progress-bar {
-            width: 100%;
-            height: 8px;
-            background: #e0e0e0;
-            border-radius: 10px;
-            overflow: hidden;
-            margin-top: 10px;
-        }
-
-        .progress-fill {
-            height: 100%;
-            background: linear-gradient(90deg, #667eea 0%, #764ba2 100%);
-            transition: width 0.3s;
-        }
-
-        .btn {
-            padding: 12px 30px;
-            border: none;
-            border-radius: 8px;
-            cursor: pointer;
-            font-size: 1em;
-            font-weight: 600;
-            transition: all 0.3s;
-        }
-
-        .btn-primary {
-            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
-            color: white;
-            box-shadow: 0 5px 15px rgba(102, 126, 234, 0.4);
-        }
-
-        .btn-primary:hover {
-            transform: translateY(-2px);
-            box-shadow: 0 7px 20px rgba(102, 126, 234, 0.6);
-        }
-
-        .btn-danger {
-            background: #f44336;
-            color: white;
-        }
-
-        .btn-danger:hover {
-            background: #d32f2f;
-        }
-
-        .btn-success {
-            background: #4caf50;
-            color: white;
-        }
-
-        .btn-success:hover {
-            background: #45a049;
-        }
-
-        .export-buttons {
-            display: flex;
-            gap: 10px;
-            margin-top: 20px;
-        }
-
-        .file-upload {
-            margin-bottom: 20px;
-        }
-
-        .file-upload input[type="file"] {
-            display: none;
-        }
-
-        .file-upload label {
-            display: inline-block;
-            padding: 12px 30px;
-            background: #667eea;
-            color: white;
-            border-radius: 8px;
-            cursor: pointer;
-            font-weight: 600;
-            transition: all 0.3s;
-        }
-
-        .file-upload label:hover {
-            background: #5568d3;
-        }
-
-        .loading {
-            text-align: center;
-            padding: 40px;
-            color: #666;
-        }
-
-        .loading::after {
-            content: '...';
-            animation: loading 1.5s infinite;
-        }
-
-        @keyframes loading {
-            0%, 20% { content: '.'; }
-            40% { content: '..'; }
-            60%, 100% { content: '...'; }
-        }
-
-        .model-selector {
-            margin-bottom: 20px;
-        }
-
-        .model-selector select {
-            width: 100%;
-            padding: 12px;
-            border: 2px solid #e0e0e0;
-            border-radius: 8px;
-            font-size: 1em;
-            background: white;
-            cursor: pointer;
-        }
-
-        .model-selector select:focus {
-            outline: none;
-            border-color: #667eea;
-        }
-    </style>
-</head>
-<body>
-    <div class="container">
-        <div class="header">
-            <h1>📊 Dashboard - Land Classification System</h1>
-            <p>Tổng quan hệ thống phân loại đất từ xa</p>
-        </div>
-
-        <div class="nav-tabs">
-            <button class="nav-tab active" onclick="switchTab('overview')">📈 Tổng Quan</button>
-            <button class="nav-tab" onclick="switchTab('trends')">📊 Accuracy Trends</button>
-            <button class="nav-tab" onclick="switchTab('batch')">🔄 Batch Processing</button>
-        </div>
-
-        <!-- Tab: Tổng Quan -->
-        <div id="overview" class="tab-content active">
-            <div class="stats-grid">
-                <div class="stat-card">
-                    <div class="icon">🤖</div>
-                    <div class="value" id="totalModels">-</div>
-                    <div class="label">Models Trained</div>
-                </div>
-                <div class="stat-card">
-                    <div class="icon">🗺️</div>
-                    <div class="value" id="totalPredictions">-</div>
-                    <div class="label">Predictions Generated</div>
-                </div>
-                <div class="stat-card">
-                    <div class="icon">📄</div>
-                    <div class="value" id="totalReports">-</div>
-                    <div class="label">Reports Created</div>
-                </div>
-                <div class="stat-card">
-                    <div class="icon">✅</div>
-                    <div class="value" id="latestAccuracy">-</div>
-                    <div class="label">Latest Model Accuracy</div>
-                </div>
-            </div>
-
-            <div class="chart-container">
-                <h3>📊 Phân bố các lớp đất (Model mới nhất)</h3>
-                <div class="model-selector">
-                    <select id="modelSelect" onchange="loadClassDistribution()">
-                        <option value="">Chọn model...</option>
-                    </select>
-                </div>
-                <div class="chart-wrapper">
-                    <canvas id="classDistChart"></canvas>
-                </div>
-                <div class="export-buttons">
-                    <button class="btn btn-primary" onclick="exportChart('classDistChart', 'class-distribution.png')">
-                        💾 Export PNG
-                    </button>
-                    <button class="btn btn-success" onclick="exportChartPDF('classDistChart', 'class-distribution.pdf')">
-                        📄 Export PDF
-                    </button>
-                </div>
-            </div>
-        </div>
-
-        <!-- Tab: Accuracy Trends -->
-        <div id="trends" class="tab-content">
-            <div class="chart-container">
-                <h3>📈 Accuracy Trends Over Time</h3>
-                <div class="chart-wrapper">
-                    <canvas id="accuracyTrendChart"></canvas>
-                </div>
-                <div class="export-buttons">
-                    <button class="btn btn-primary" onclick="exportChart('accuracyTrendChart', 'accuracy-trends.png')">
-                        💾 Export PNG
-                    </button>
-                    <button class="btn btn-success" onclick="exportChartPDF('accuracyTrendChart', 'accuracy-trends.pdf')">
-                        📄 Export PDF
-                    </button>
-                </div>
-            </div>
-
-            <div class="chart-container">
-                <h3>📊 F1-Score Comparison</h3>
-                <div class="chart-wrapper">
-                    <canvas id="f1ScoreChart"></canvas>
-                </div>
-            </div>
-        </div>
-
-        <!-- Tab: Batch Processing -->
-        <div id="batch" class="tab-content">
-            <div class="batch-queue">
-                <h3>🔄 Batch Prediction Queue</h3>
-                
-                <div class="file-upload">
-                    <label for="csvFile">📁 Upload CSV File</label>
-                    <input type="file" id="csvFile" accept=".csv" onchange="handleCSVUpload(event)">
-                    <p style="margin-top: 10px; color: #666;">
-                        Format CSV: name,min_lon,min_lat,max_lon,max_lat
-                    </p>
-                </div>
-
-                <div class="model-selector">
-                    <select id="batchModelSelect">
-                        <option value="">Chọn model để predict...</option>
-                    </select>
-                </div>
-
-                <button class="btn btn-primary" onclick="startBatchPrediction()" style="margin-bottom: 30px;">
-                    🚀 Start Batch Prediction
-                </button>
-
-                <h4 style="margin: 20px 0;">Queue Status</h4>
-                <div class="stats-grid" style="margin-bottom: 30px;">
-                    <div class="stat-card">
-                        <div class="value" id="queuedJobs">0</div>
-                        <div class="label">⏳ Queued</div>
-                    </div>
-                    <div class="stat-card">
-                        <div class="value" id="runningJobs">0</div>
-                        <div class="label">▶️ Running</div>
-                    </div>
-                    <div class="stat-card">
-                        <div class="value" id="completedJobs">0</div>
-                        <div class="label">✅ Completed</div>
-                    </div>
-                    <div class="stat-card">
-                        <div class="value" id="failedJobs">0</div>
-                        <div class="label">❌ Failed</div>
-                    </div>
-                </div>
-
-                <h4 style="margin: 20px 0;">Active Jobs</h4>
-                <div id="batchJobs">
-                    <p class="loading">Đang tải dữ liệu</p>
-                </div>
-            </div>
-        </div>
-    </div>
-
-    <script src="https://cdn.jsdelivr.net/npm/chart.js@4.4.0/dist/chart.umd.min.js"></script>
-    <script src="https://cdnjs.cloudflare.com/ajax/libs/jspdf/2.5.1/jspdf.umd.min.js"></script>
-
-    <script>
-        let charts = {};
-        let batchItems = [];
-        let refreshInterval = null;
-
-        // Tab switching
-        function switchTab(tabName) {
-            // Update tab buttons
-            document.querySelectorAll('.nav-tab').forEach(tab => {
-                tab.classList.remove('active');
-            });
-            event.target.classList.add('active');
-
-            // Update tab content
-            document.querySelectorAll('.tab-content').forEach(content => {
-                content.classList.remove('active');
-            });
-            document.getElementById(tabName).classList.add('active');
-
-            // Load data for the active tab
-            if (tabName === 'overview') {
-                loadDashboardStats();
-            } else if (tabName === 'trends') {
-                loadAccuracyTrends();
-            } else if (tabName === 'batch') {
-                loadBatchStatus();
-                startBatchRefresh();
-            } else {
-                stopBatchRefresh();
-            }
-        }
-
-        // Load dashboard statistics
-        async function loadDashboardStats() {
-            try {
-                const response = await fetch('/api/dashboard/statistics');
-                const data = await response.json();
-
-                document.getElementById('totalModels').textContent = data.models.total;
-                document.getElementById('totalPredictions').textContent = data.predictions.total;
-                document.getElementById('totalReports').textContent = data.reports.total;
-
-                if (data.models.latest && data.models.latest.metrics) {
-                    const accuracy = (data.models.latest.metrics.accuracy * 100).toFixed(2);
-                    document.getElementById('latestAccuracy').textContent = accuracy + '%';
-                }
-
-                // Load models for selector
-                await loadModelsList();
-            } catch (error) {
-                console.error('Error loading dashboard stats:', error);
-            }
-        }
-
-        // Load models list
-        async function loadModelsList() {
-            try {
-                const response = await fetch('/api/models/list');
-                const data = await response.json();
-
-                const modelSelect = document.getElementById('modelSelect');
-                const batchModelSelect = document.getElementById('batchModelSelect');
-
-                modelSelect.innerHTML = '<option value="">Chọn model...</option>';
-                batchModelSelect.innerHTML = '<option value="">Chọn model...</option>';
-
-                data.models.forEach(model => {
-                    const option = document.createElement('option');
-                    option.value = model.filename;
-                    option.textContent = `${model.filename} (${model.created})`;
-                    modelSelect.appendChild(option.cloneNode(true));
-                    batchModelSelect.appendChild(option);
-                });
-
-                // Auto-select latest model
-                if (data.models.length > 0) {
-                    modelSelect.value = data.models[0].filename;
-                    await loadClassDistribution();
-                }
-            } catch (error) {
-                console.error('Error loading models:', error);
-            }
-        }
-
-        // Load class distribution
-        async function loadClassDistribution() {
-            const modelFilename = document.getElementById('modelSelect').value;
-            if (!modelFilename) return;
-
-            try {
-                const response = await fetch(`/api/dashboard/class-distribution/${modelFilename}`);
-                const data = await response.json();
-
-                const labels = Object.keys(data.class_distribution);
-                const values = Object.values(data.class_distribution);
-
-                if (charts.classDistChart) {
-                    charts.classDistChart.destroy();
-                }
-
-                const ctx = document.getElementById('classDistChart').getContext('2d');
-                charts.classDistChart = new Chart(ctx, {
-                    type: 'bar',
-                    data: {
-                        labels: labels,
-                        datasets: [{
-                            label: 'Số lượng mẫu',
-                            data: values,
-                            backgroundColor: [
-                                'rgba(102, 126, 234, 0.7)',
-                                'rgba(118, 75, 162, 0.7)',
-                                'rgba(76, 175, 80, 0.7)',
-                                'rgba(244, 67, 54, 0.7)',
-                                'rgba(33, 150, 243, 0.7)',
-                                'rgba(255, 193, 7, 0.7)',
-                            ],
-                            borderColor: [
-                                'rgba(102, 126, 234, 1)',
-                                'rgba(118, 75, 162, 1)',
-                                'rgba(76, 175, 80, 1)',
-                                'rgba(244, 67, 54, 1)',
-                                'rgba(33, 150, 243, 1)',
-                                'rgba(255, 193, 7, 1)',
-                            ],
-                            borderWidth: 2
-                        }]
-                    },
-                    options: {
-                        responsive: true,
-                        maintainAspectRatio: false,
-                        plugins: {
-                            legend: {
-                                display: false
-                            },
-                            title: {
-                                display: true,
-                                text: `Tổng: ${data.total_samples} mẫu`
-                            }
-                        },
-                        scales: {
-                            y: {
-                                beginAtZero: true
-                            }
-                        }
-                    }
-                });
-            } catch (error) {
-                console.error('Error loading class distribution:', error);
-            }
-        }
-
-        // Load accuracy trends
-        async function loadAccuracyTrends() {
-            try {
-                const response = await fetch('/api/dashboard/accuracy-trends');
-                const data = await response.json();
-
-                if (data.trends.length === 0) {
-                    return;
-                }
-
-                // Prepare data
-                const labels = data.trends.map(d => new Date(d.date).toLocaleDateString('vi-VN'));
-                const accuracies = data.trends.map(d => d.accuracy * 100);
-                const f1Scores = data.trends.map(d => d.f1_score * 100);
-                const precisions = data.trends.map(d => d.precision * 100);
-                const recalls = data.trends.map(d => d.recall * 100);
-
-                // Accuracy Trend Chart
-                if (charts.accuracyTrendChart) {
-                    charts.accuracyTrendChart.destroy();
-                }
-
-                const ctx1 = document.getElementById('accuracyTrendChart').getContext('2d');
-                charts.accuracyTrendChart = new Chart(ctx1, {
-                    type: 'line',
-                    data: {
-                        labels: labels,
-                        datasets: [
-                            {
-                                label: 'Accuracy (%)',
-                                data: accuracies,
-                                borderColor: 'rgba(102, 126, 234, 1)',
-                                backgroundColor: 'rgba(102, 126, 234, 0.1)',
-                                fill: true,
-                                tension: 0.4
-                            },
-                            {
-                                label: 'Precision (%)',
-                                data: precisions,
-                                borderColor: 'rgba(76, 175, 80, 1)',
-                                backgroundColor: 'rgba(76, 175, 80, 0.1)',
-                                fill: false,
-                                tension: 0.4
-                            },
-                            {
-                                label: 'Recall (%)',
-                                data: recalls,
-                                borderColor: 'rgba(244, 67, 54, 1)',
-                                backgroundColor: 'rgba(244, 67, 54, 0.1)',
-                                fill: false,
-                                tension: 0.4
-                            }
-                        ]
-                    },
-                    options: {
-                        responsive: true,
-                        maintainAspectRatio: false,
-                        plugins: {
-                            legend: {
-                                display: true,
-                                position: 'top'
-                            }
-                        },
-                        scales: {
-                            y: {
-                                beginAtZero: true,
-                                max: 100,
-                                ticks: {
-                                    callback: function(value) {
-                                        return value + '%';
-                                    }
-                                }
-                            }
-                        }
-                    }
-                });
-
-                // F1-Score Chart
-                if (charts.f1ScoreChart) {
-                    charts.f1ScoreChart.destroy();
-                }
-
-                const ctx2 = document.getElementById('f1ScoreChart').getContext('2d');
-                charts.f1ScoreChart = new Chart(ctx2, {
-                    type: 'bar',
-                    data: {
-                        labels: labels,
-                        datasets: [{
-                            label: 'F1-Score (%)',
-                            data: f1Scores,
-                            backgroundColor: 'rgba(118, 75, 162, 0.7)',
-                            borderColor: 'rgba(118, 75, 162, 1)',
-                            borderWidth: 2
-                        }]
-                    },
-                    options: {
-                        responsive: true,
-                        maintainAspectRatio: false,
-                        scales: {
-                            y: {
-                                beginAtZero: true,
-                                max: 100,
-                                ticks: {
-                                    callback: function(value) {
-                                        return value + '%';
-                                    }
-                                }
-                            }
-                        }
-                    }
-                });
-            } catch (error) {
-                console.error('Error loading accuracy trends:', error);
-            }
-        }
-
-        // Export chart as PNG
-        function exportChart(chartId, filename) {
-            const canvas = document.getElementById(chartId);
-            const url = canvas.toDataURL('image/png');
-            const link = document.createElement('a');
-            link.download = filename;
-            link.href = url;
-            link.click();
-        }
-
-        // Export chart as PDF
-        function exportChartPDF(chartId, filename) {
-            const canvas = document.getElementById(chartId);
-            const imgData = canvas.toDataURL('image/png');
-            
-            const { jsPDF } = window.jspdf;
-            const pdf = new jsPDF({
-                orientation: 'landscape',
-                unit: 'px',
-                format: [canvas.width, canvas.height]
-            });
-            
-            pdf.addImage(imgData, 'PNG', 0, 0, canvas.width, canvas.height);
-            pdf.save(filename);
-        }
-
-        // Handle CSV upload
-        function handleCSVUpload(event) {
-            const file = event.target.files[0];
-            if (!file) return;
-
-            const reader = new FileReader();
-            reader.onload = function(e) {
-                const text = e.target.result;
-                parseCSV(text);
-            };
-            reader.readAsText(file);
-        }
-
-        // Parse CSV
-        function parseCSV(text) {
-            const lines = text.trim().split('\n');
-            batchItems = [];
-
-            // Skip header
-            for (let i = 1; i < lines.length; i++) {
-                const parts = lines[i].split(',');
-                if (parts.length >= 5) {
-                    batchItems.push({
-                        name: parts[0].trim(),
-                        min_lon: parseFloat(parts[1]),
-                        min_lat: parseFloat(parts[2]),
-                        max_lon: parseFloat(parts[3]),
-                        max_lat: parseFloat(parts[4]),
-                        start_date: parts[5]?.trim() || "2023-03-01",
-                        end_date: parts[6]?.trim() || "2023-05-31",
-                        max_scenes: parseInt(parts[7]) || 12,
-                        cloud_cover: parseInt(parts[8]) || 30,
-                        resolution: parseInt(parts[9]) || 20
-                    });
-                }
-            }
-
-            alert(`✅ Đã tải ${batchItems.length} khu vực từ CSV`);
-        }
-
-        // Start batch prediction
-        async function startBatchPrediction() {
-            const modelFilename = document.getElementById('batchModelSelect').value;
-            
-            if (!modelFilename) {
-                alert('❌ Vui lòng chọn model');
-                return;
-            }
-
-            if (batchItems.length === 0) {
-                alert('❌ Vui lòng upload file CSV trước');
-                return;
-            }
-
-            try {
-                const response = await fetch('/api/batch/start', {
-                    method: 'POST',
-                    headers: {
-                        'Content-Type': 'application/json'
-                    },
-                    body: JSON.stringify({
-                        model_filename: modelFilename,
-                        items: batchItems,
-                        auto_retry: true,
-                        max_retries: 3
-                    })
-                });
-
-                const result = await response.json();
-                alert(`✅ ${result.message}`);
-                
-                // Refresh batch status
-                loadBatchStatus();
-            } catch (error) {
-                console.error('Error starting batch:', error);
-                alert('❌ Lỗi khi bắt đầu batch prediction');
-            }
-        }
-
-        // Load batch status
-        async function loadBatchStatus() {
-            try {
-                const response = await fetch('/api/batch/status');
-                const data = await response.json();
-
-                // Update counters
-                document.getElementById('queuedJobs').textContent = data.queue.queued;
-                document.getElementById('runningJobs').textContent = data.queue.running;
-                document.getElementById('completedJobs').textContent = data.queue.completed;
-                document.getElementById('failedJobs').textContent = data.queue.failed;
-
-                // Display jobs
-                const jobsContainer = document.getElementById('batchJobs');
-                jobsContainer.innerHTML = '';
-
-                // Combine all jobs
-                const allJobs = [
-                    ...data.jobs.running,
-                    ...data.jobs.queued,
-                    ...data.jobs.recent_completed,
-                    ...data.jobs.recent_failed
-                ];
-
-                if (allJobs.length === 0) {
-                    jobsContainer.innerHTML = '<p style="text-align: center; color: #666;">Chưa có job nào</p>';
-                    return;
-                }
-
-                allJobs.forEach(job => {
-                    const jobElement = document.createElement('div');
-                    jobElement.className = `batch-item ${job.status}`;
-                    
-                    const progress = job.progress || 0;
-                    const errorMsg = job.error ? `<p style="color: #f44336; margin-top: 10px;">⚠️ ${job.error}</p>` : '';
-                    
-                    jobElement.innerHTML = `
-                        <div class="batch-header">
-                            <div class="batch-name">${job.name}</div>
-                            <div class="batch-status ${job.status}">${job.status.toUpperCase()}</div>
-                        </div>
-                        <p style="color: #666; margin: 5px 0;">Job ID: ${job.job_id}</p>
-                        <p style="color: #666; margin: 5px 0;">
-                            📍 [${job.config.min_lon.toFixed(2)}, ${job.config.min_lat.toFixed(2)}] → 
-                            [${job.config.max_lon.toFixed(2)}, ${job.config.max_lat.toFixed(2)}]
-                        </p>
-                        ${job.retries > 0 ? `<p style="color: #ff9800; margin: 5px 0;">🔄 Retries: ${job.retries}/${job.max_retries}</p>` : ''}
-                        ${errorMsg}
-                        <div class="progress-bar">
-                            <div class="progress-fill" style="width: ${progress}%"></div>
-                        </div>
-                    `;
-                    
-                    jobsContainer.appendChild(jobElement);
-                });
-            } catch (error) {
-                console.error('Error loading batch status:', error);
-            }
-        }
-
-        // Auto-refresh batch status
-        function startBatchRefresh() {
-            if (refreshInterval) return;
-            refreshInterval = setInterval(loadBatchStatus, 3000);
-        }
-
-        function stopBatchRefresh() {
-            if (refreshInterval) {
-                clearInterval(refreshInterval);
-                refreshInterval = null;
-            }
-        }
-
-        // Initialize on page load
-        window.onload = function() {
-            loadDashboardStats();
-        };
-
-        // Cleanup on page unload
-        window.onbeforeunload = function() {
-            stopBatchRefresh();
-        };
-    </script>
-</body>
-</html>

dataset_cache/training_data_59838d7be931abe93b5dd38e7cd89ad7.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7318566231680cb0c97883b7a5e4177aa1bfeec462a0f52ed34aa103ddec210b
+size 20903

dataset_cache/training_data_73f65eba2eb052d78cdbf76250e1e68a.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9cabf5e0241dcc3a73133ac8ae11171f34042c491f895b268c016407619bdfe1
+size 20903

feature_extractor.py

@@ -1,454 +0,0 @@
-"""
-Feature Extraction Module for Land Classification
-Chuẩn hóa việc trích xuất features từ satellite data cho cả training và prediction
-"""
-
-import numpy as np
-import xarray as xr
-from typing import List, Dict, Tuple, Optional
-
-
-class FeatureExtractor:
-    """
-    Extract features từ Sentinel-2 và Sentinel-1 data
-    Hỗ trợ 2 modes:
-    - 'simple': 3 features cơ bản (NDVI_mean, VH_mean, VV_mean)
-    - 'temporal': 39 features time-series (NDVI + NDWI + NDBI theo thời gian)
-    """
-    
-    FEATURE_MODES = {
-        'simple': {
-            'n_features': 3,
-            'features': ['NDVI_mean', 'VH_db_mean', 'VV_db_mean'],
-            'description': 'Simple aggregate features (mean only)'
-        },
-        'temporal': {
-            'n_features': 39,
-            'features': None,  # Generated dynamically based on time steps
-            'description': 'Temporal features with NDVI, NDWI, NDBI time series'
-        },
-        'extended': {
-            'n_features': 15,
-            'features': [
-                'NDVI_mean', 'NDVI_std', 'NDVI_min', 'NDVI_max',
-                'NDWI_mean', 'NDWI_std', 'NDWI_min', 'NDWI_max',
-                'NDBI_mean', 'NDBI_std', 'NDBI_min', 'NDBI_max',
-                'VH_db_mean', 'VV_db_mean', 'VH_VV_ratio'
-            ],
-            'description': 'Extended aggregate features with statistics'
-        },
-        'odc': {
-            'n_features': 8,
-            'features': [
-                'ndvi_mean', 'ndvi_min', 'ndvi_max', 'ndvi_std', 'ndvi_range',
-                'ndwi_mean', 'ndbi_mean', 'evi_mean'
-            ],
-            'description': 'ODC mode: 8 aggregate features (NDVI stats + NDWI/NDBI/EVI mean) - matches 01.train_ODC.ipynb'
-        }
-    }
-    
-    def __init__(self, mode: str = 'simple'):
-        """
-        Initialize FeatureExtractor
-        
-        Args:
-            mode: 'simple', 'temporal', hoặc 'extended'
-        """
-        if mode not in self.FEATURE_MODES:
-            raise ValueError(f"Invalid mode: {mode}. Choose from {list(self.FEATURE_MODES.keys())}")
-        
-        self.mode = mode
-        self.config = self.FEATURE_MODES[mode]
-    
-    def get_feature_names(self, n_timesteps: Optional[int] = None) -> List[str]:
-        """
-        Lấy danh sách tên features
-        
-        Args:
-            n_timesteps: Số timesteps (chỉ cần cho mode='temporal')
-        
-        Returns:
-            List tên features
-        """
-        if self.mode == 'temporal':
-            if n_timesteps is None:
-                raise ValueError("n_timesteps required for temporal mode")
-            
-            features = []
-            # NDVI time series
-            for t in range(n_timesteps):
-                features.append(f'NDVI_t{t+1}')
-            # NDWI time series
-            for t in range(n_timesteps):
-                features.append(f'NDWI_t{t+1}')
-            # NDBI time series
-            for t in range(n_timesteps):
-                features.append(f'NDBI_t{t+1}')
-            
-            # VH/VV radar (mean across time)
-            features.append('VH_db_mean')
-            features.append('VV_db_mean')
-            features.append('VH_VV_ratio')
-            
-            return features
-        else:
-            return self.config['features']
-    
-    def extract_simple_features(
-        self, 
-        ndvi_data: xr.DataArray,
-        vh_data: Optional[xr.DataArray] = None,
-        vv_data: Optional[xr.DataArray] = None
-    ) -> np.ndarray:
-        """
-        Extract simple features (3 features: NDVI_mean, VH_db_mean, VV_db_mean)
-        
-        Args:
-            ndvi_data: NDVI DataArray (có thể có time dimension)
-            vh_data: VH radar DataArray
-            vv_data: VV radar DataArray
-        
-        Returns:
-            Feature array shape (n_pixels, 3)
-        """
-        # Calculate NDVI mean
-        if 'time' in ndvi_data.dims:
-            ndvi_mean = ndvi_data.mean(dim='time')
-        else:
-            ndvi_mean = ndvi_data
-        
-        # Flatten to pixels
-        ndvi_flat = ndvi_mean.values.flatten()
-        
-        # Calculate radar features if available
-        if vh_data is not None and vv_data is not None:
-            if 'time' in vh_data.dims:
-                vh_mean = vh_data.mean(dim='time')
-                vv_mean = vv_data.mean(dim='time')
-            else:
-                vh_mean = vh_data
-                vv_mean = vv_data
-            
-            vh_flat = vh_mean.values.flatten()
-            vv_flat = vv_mean.values.flatten()
-        else:
-            # If no radar data, use zeros
-            vh_flat = np.zeros_like(ndvi_flat)
-            vv_flat = np.zeros_like(ndvi_flat)
-        
-        # Stack features
-        features = np.column_stack([ndvi_flat, vh_flat, vv_flat])
-        
-        return features
-    
-    def extract_temporal_features(
-        self,
-        s2_data: xr.Dataset,
-        vh_data: Optional[xr.DataArray] = None,
-        vv_data: Optional[xr.DataArray] = None
-    ) -> np.ndarray:
-        """
-        Extract temporal features (39 features: time series của NDVI, NDWI, NDBI + radar)
-        
-        Args:
-            s2_data: Sentinel-2 Dataset với bands B02, B03, B04, B08, B11
-            vh_data: VH radar DataArray
-            vv_data: VV radar DataArray
-        
-        Returns:
-            Feature array shape (n_pixels, 39)
-        """
-        # Calculate spectral indices
-        nir = s2_data["B08"].astype('float32')
-        red = s2_data["B04"].astype('float32')
-        green = s2_data["B03"].astype('float32')
-        swir = s2_data["B11"].astype('float32') if "B11" in s2_data else s2_data["B02"]  # Fallback to B02
-        
-        # NDVI = (NIR - Red) / (NIR + Red)
-        ndvi = (nir - red) / (nir + red + 1e-8)
-        
-        # NDWI = (Green - NIR) / (Green + NIR)
-        ndwi = (green - nir) / (green + nir + 1e-8)
-        
-        # NDBI = (SWIR - NIR) / (SWIR + NIR)
-        ndbi = (swir - nir) / (swir + nir + 1e-8)
-        
-        # Resample to monthly if time dimension exists
-        if 'time' in ndvi.dims:
-            ndvi_monthly = ndvi.resample(time="1ME").mean()
-            ndwi_monthly = ndwi.resample(time="1ME").mean()
-            ndbi_monthly = ndbi.resample(time="1ME").mean()
-        else:
-            ndvi_monthly = ndvi
-            ndwi_monthly = ndwi
-            ndbi_monthly = ndbi
-        
-        # Get dimensions
-        n_times = len(ndvi_monthly.time) if 'time' in ndvi_monthly.dims else 1
-        y_size = len(ndvi_monthly.y)
-        x_size = len(ndvi_monthly.x)
-        n_pixels = y_size * x_size
-        
-        # Extract temporal features
-        features_list = []
-        
-        # NDVI time series
-        for t in range(n_times):
-            if 'time' in ndvi_monthly.dims:
-                ndvi_t = ndvi_monthly.isel(time=t).values.flatten()
-            else:
-                ndvi_t = ndvi_monthly.values.flatten()
-            features_list.append(ndvi_t)
-        
-        # NDWI time series
-        for t in range(n_times):
-            if 'time' in ndwi_monthly.dims:
-                ndwi_t = ndwi_monthly.isel(time=t).values.flatten()
-            else:
-                ndwi_t = ndwi_monthly.values.flatten()
-            features_list.append(ndwi_t)
-        
-        # NDBI time series
-        for t in range(n_times):
-            if 'time' in ndbi_monthly.dims:
-                ndbi_t = ndbi_monthly.isel(time=t).values.flatten()
-            else:
-                ndbi_t = ndbi_monthly.values.flatten()
-            features_list.append(ndbi_t)
-        
-        # Stack all spectral features
-        features = np.column_stack(features_list)
-        
-        # Add radar features if available
-        if vh_data is not None and vv_data is not None:
-            if 'time' in vh_data.dims:
-                vh_mean = vh_data.mean(dim='time')
-                vv_mean = vv_data.mean(dim='time')
-            else:
-                vh_mean = vh_data
-                vv_mean = vv_data
-            
-            vh_flat = vh_mean.values.flatten()
-            vv_flat = vv_mean.values.flatten()
-            vh_vv_ratio = vh_flat / (vv_flat + 1e-8)
-            
-            # Add radar features
-            features = np.column_stack([features, vh_flat, vv_flat, vh_vv_ratio])
-        
-        return features
-    
-    def extract_odc_features(
-        self,
-        s2_data: xr.Dataset,
-        vh_data: Optional[xr.DataArray] = None,
-        vv_data: Optional[xr.DataArray] = None
-    ) -> np.ndarray:
-        """
-        Extract ODC aggregate features (8 features matching 01.train_ODC.ipynb):
-        ndvi_mean, ndvi_min, ndvi_max, ndvi_std, ndvi_range, ndwi_mean, ndbi_mean, evi_mean
-        
-        Args:
-            s2_data: Sentinel-2 Dataset with B02, B03, B04, B08, B11
-            vh_data: Not used in ODC mode
-            vv_data: Not used in ODC mode
-        
-        Returns:
-            Feature array shape (n_pixels, 8)
-        """
-        # Calculate spectral indices
-        nir = s2_data["B08"].astype('float32')
-        red = s2_data["B04"].astype('float32')
-        green = s2_data["B03"].astype('float32')
-        blue = s2_data["B02"].astype('float32')
-        swir = s2_data["B11"].astype('float32') if "B11" in s2_data else s2_data["B02"]
-        
-        # NDVI = (NIR - Red) / (NIR + Red)
-        ndvi = (nir - red) / (nir + red + 1e-8)
-        
-        # NDWI = (Green - NIR) / (Green + NIR)
-        ndwi = (green - nir) / (green + nir + 1e-8)
-        
-        # NDBI = (SWIR - NIR) / (SWIR + NIR)
-        ndbi = (swir - nir) / (swir + nir + 1e-8)
-        
-        # EVI = 2.5 * (NIR - Red) / (NIR + 6*Red - 7.5*Blue + 1)
-        evi = 2.5 * (nir - red) / (nir + 6*red - 7.5*blue + 1)
-        
-        features_list = []
-        
-        # NDVI statistics (5 features)
-        if 'time' in ndvi.dims:
-            features_list.append(ndvi.mean(dim='time').values.flatten())  # ndvi_mean
-            features_list.append(ndvi.min(dim='time').values.flatten())   # ndvi_min
-            features_list.append(ndvi.max(dim='time').values.flatten())   # ndvi_max
-            features_list.append(ndvi.std(dim='time').values.flatten())   # ndvi_std
-            ndvi_range = (ndvi.max(dim='time') - ndvi.min(dim='time')).values.flatten()
-            features_list.append(ndvi_range)  # ndvi_range
-        else:
-            ndvi_flat = ndvi.values.flatten()
-            features_list.extend([ndvi_flat, ndvi_flat, ndvi_flat, np.zeros_like(ndvi_flat), np.zeros_like(ndvi_flat)])
-        
-        # NDWI mean (1 feature)
-        if 'time' in ndwi.dims:
-            features_list.append(ndwi.mean(dim='time').values.flatten())  # ndwi_mean
-        else:
-            features_list.append(ndwi.values.flatten())
-        
-        # NDBI mean (1 feature)
-        if 'time' in ndbi.dims:
-            features_list.append(ndbi.mean(dim='time').values.flatten())  # ndbi_mean
-        else:
-            features_list.append(ndbi.values.flatten())
-        
-        # EVI mean (1 feature)
-        if 'time' in evi.dims:
-            features_list.append(evi.mean(dim='time').values.flatten())  # evi_mean
-        else:
-            features_list.append(evi.values.flatten())
-        
-        # Stack all features (total: 8 features)
-        features = np.column_stack(features_list)
-        
-        return features
-    
-    def extract_extended_features(
-        self,
-        s2_data: xr.Dataset,
-        vh_data: Optional[xr.DataArray] = None,
-        vv_data: Optional[xr.DataArray] = None
-    ) -> np.ndarray:
-        """
-        Extract extended aggregate features (15 features: stats của NDVI, NDWI, NDBI + radar)
-        
-        Args:
-            s2_data: Sentinel-2 Dataset
-            vh_data: VH radar DataArray
-            vv_data: VV radar DataArray
-        
-        Returns:
-            Feature array shape (n_pixels, 15)
-        """
-        # Calculate spectral indices
-        nir = s2_data["B08"].astype('float32')
-        red = s2_data["B04"].astype('float32')
-        green = s2_data["B03"].astype('float32')
-        swir = s2_data["B11"].astype('float32') if "B11" in s2_data else s2_data["B02"]
-        
-        ndvi = (nir - red) / (nir + red + 1e-8)
-        ndwi = (green - nir) / (green + nir + 1e-8)
-        ndbi = (swir - nir) / (swir + nir + 1e-8)
-        
-        features_list = []
-        
-        # NDVI statistics
-        if 'time' in ndvi.dims:
-            features_list.append(ndvi.mean(dim='time').values.flatten())
-            features_list.append(ndvi.std(dim='time').values.flatten())
-            features_list.append(ndvi.min(dim='time').values.flatten())
-            features_list.append(ndvi.max(dim='time').values.flatten())
-        else:
-            ndvi_flat = ndvi.values.flatten()
-            features_list.extend([ndvi_flat, np.zeros_like(ndvi_flat), ndvi_flat, ndvi_flat])
-        
-        # NDWI statistics
-        if 'time' in ndwi.dims:
-            features_list.append(ndwi.mean(dim='time').values.flatten())
-            features_list.append(ndwi.std(dim='time').values.flatten())
-            features_list.append(ndwi.min(dim='time').values.flatten())
-            features_list.append(ndwi.max(dim='time').values.flatten())
-        else:
-            ndwi_flat = ndwi.values.flatten()
-            features_list.extend([ndwi_flat, np.zeros_like(ndwi_flat), ndwi_flat, ndwi_flat])
-        
-        # NDBI statistics
-        if 'time' in ndbi.dims:
-            features_list.append(ndbi.mean(dim='time').values.flatten())
-            features_list.append(ndbi.std(dim='time').values.flatten())
-            features_list.append(ndbi.min(dim='time').values.flatten())
-            features_list.append(ndbi.max(dim='time').values.flatten())
-        else:
-            ndbi_flat = ndbi.values.flatten()
-            features_list.extend([ndbi_flat, np.zeros_like(ndbi_flat), ndbi_flat, ndbi_flat])
-        
-        # Stack spectral features
-        features = np.column_stack(features_list)
-        
-        # Add radar features
-        if vh_data is not None and vv_data is not None:
-            if 'time' in vh_data.dims:
-                vh_mean = vh_data.mean(dim='time')
-                vv_mean = vv_data.mean(dim='time')
-            else:
-                vh_mean = vh_data
-                vv_mean = vv_data
-            
-            vh_flat = vh_mean.values.flatten()
-            vv_flat = vv_mean.values.flatten()
-            vh_vv_ratio = vh_flat / (vv_flat + 1e-8)
-            
-            features = np.column_stack([features, vh_flat, vv_flat, vh_vv_ratio])
-        
-        return features
-    
-    def extract(
-        self,
-        s2_data: Optional[xr.Dataset] = None,
-        ndvi_data: Optional[xr.DataArray] = None,
-        vh_data: Optional[xr.DataArray] = None,
-        vv_data: Optional[xr.DataArray] = None
-    ) -> np.ndarray:
-        """
-        Extract features theo mode đã chọn
-        
-        Args:
-            s2_data: Sentinel-2 Dataset (cần cho temporal, extended, và odc modes)
-            ndvi_data: NDVI DataArray (cần cho simple mode)
-            vh_data: VH radar DataArray
-            vv_data: VV radar DataArray
-        
-        Returns:
-            Feature array
-        """
-        if self.mode == 'simple':
-            if ndvi_data is None:
-                raise ValueError("ndvi_data required for simple mode")
-            return self.extract_simple_features(ndvi_data, vh_data, vv_data)
-        
-        elif self.mode == 'temporal':
-            if s2_data is None:
-                raise ValueError("s2_data required for temporal mode")
-            return self.extract_temporal_features(s2_data, vh_data, vv_data)
-        
-        elif self.mode == 'extended':
-            if s2_data is None:
-                raise ValueError("s2_data required for extended mode")
-            return self.extract_extended_features(s2_data, vh_data, vv_data)
-        
-        elif self.mode == 'odc':
-            if s2_data is None:
-                raise ValueError("s2_data required for odc mode")
-            return self.extract_odc_features(s2_data, vh_data, vv_data)
-        
-        else:
-            raise ValueError(f"Unknown mode: {self.mode}")
-    
-    def get_info(self) -> Dict:
-        """Lấy thông tin về feature extraction mode"""
-        return {
-            'mode': self.mode,
-            'n_features': self.config['n_features'],
-            'description': self.config['description']
-        }
-
-
-def get_feature_extractor(mode: str = 'simple') -> FeatureExtractor:
-    """
-    Factory function để tạo FeatureExtractor
-    
-    Args:
-        mode: 'simple', 'temporal', 'extended', hoặc 'odc'
-    
-    Returns:
-        FeatureExtractor instance
-    """
-    return FeatureExtractor(mode=mode)

generate_previews.py

@@ -1,132 +0,0 @@
-#!/usr/bin/env python3
-"""
-Generate PNG previews for existing GeoTIFF prediction files
-"""
-
-import numpy as np
-import rasterio
-import matplotlib
-matplotlib.use('Agg')
-import matplotlib.pyplot as plt
-from pathlib import Path
-import sys
-
-def generate_png_preview(tif_file, output_png=None):
-    """Generate PNG preview from GeoTIFF file"""
-    tif_path = Path(tif_file)
-    
-    if not tif_path.exists():
-        print(f"❌ File not found: {tif_file}")
-        return False
-    
-    # Determine output PNG path
-    if output_png is None:
-        output_png = tif_path.with_suffix('.png')
-    else:
-        output_png = Path(output_png)
-    
-    try:
-        # Read GeoTIFF
-        with rasterio.open(tif_path) as src:
-            data = src.read(1)
-            
-        print(f"📊 Data shape: {data.shape}, range: [{np.nanmin(data):.3f}, {np.nanmax(data):.3f}]")
-        
-        # Determine if it's classification or NDVI based on filename
-        is_classification = 'classification' in tif_path.name.lower() or 'prediction' in tif_path.name.lower()
-        is_ndvi = 'ndvi' in tif_path.name.lower()
-        
-        # Create figure
-        fig, ax = plt.subplots(figsize=(12, 10), dpi=150)
-        
-        if is_ndvi:
-            # NDVI: use RdYlGn colormap, range -1 to 1
-            im = ax.imshow(data, cmap='RdYlGn', vmin=-1, vmax=1, interpolation='nearest')
-            ax.set_title(f'NDVI - {tif_path.stem}', fontsize=14, fontweight='bold')
-            cbar_label = 'NDVI'
-        elif is_classification:
-            # Classification: use tab20 colormap
-            im = ax.imshow(data, cmap='tab20', interpolation='nearest')
-            ax.set_title(f'Land Classification - {tif_path.stem}', fontsize=14, fontweight='bold')
-            cbar_label = 'Class'
-        else:
-            # Generic: use viridis
-            im = ax.imshow(data, cmap='viridis', interpolation='nearest')
-            ax.set_title(f'{tif_path.stem}', fontsize=14, fontweight='bold')
-            cbar_label = 'Value'
-        
-        ax.set_xlabel('X (pixels)', fontsize=10)
-        ax.set_ylabel('Y (pixels)', fontsize=10)
-        
-        # Add colorbar
-        cbar = plt.colorbar(im, ax=ax, fraction=0.046, pad=0.04)
-        cbar.set_label(cbar_label, rotation=270, labelpad=15)
-        
-        # For classification, try to set integer ticks
-        if is_classification:
-            try:
-                unique_vals = np.unique(data[~np.isnan(data)])
-                if len(unique_vals) < 20:  # Only if not too many classes
-                    cbar.set_ticks(unique_vals)
-                    cbar.set_ticklabels([str(int(v)) for v in unique_vals])
-            except:
-                pass
-        
-        # Add grid
-        ax.grid(True, alpha=0.3, linestyle='--', linewidth=0.5)
-        
-        # Save PNG
-        plt.tight_layout()
-        plt.savefig(str(output_png), dpi=150, bbox_inches='tight')
-        plt.close(fig)
-        
-        print(f"✅ Created PNG: {output_png}")
-        return True
-        
-    except Exception as e:
-        print(f"❌ Error creating PNG: {e}")
-        import traceback
-        traceback.print_exc()
-        return False
-
-
-def generate_all_previews(predictions_dir="predictions"):
-    """Generate PNG previews for all GeoTIFF files without PNGs"""
-    pred_path = Path(predictions_dir)
-    
-    if not pred_path.exists():
-        print(f"❌ Directory not found: {predictions_dir}")
-        return
-    
-    tif_files = list(pred_path.glob("*.tif"))
-    print(f"🔍 Found {len(tif_files)} GeoTIFF files")
-    
-    generated = 0
-    skipped = 0
-    
-    for tif_file in tif_files:
-        png_file = tif_file.with_suffix('.png')
-        
-        if png_file.exists():
-            print(f"⏭️  Skipping {tif_file.name} (PNG already exists)")
-            skipped += 1
-            continue
-        
-        print(f"\n🎨 Processing {tif_file.name}...")
-        if generate_png_preview(tif_file):
-            generated += 1
-    
-    print(f"\n{'='*60}")
-    print(f"✅ Generated {generated} new PNG previews")
-    print(f"⏭️  Skipped {skipped} files (already have PNGs)")
-    print(f"{'='*60}")
-
-
-if __name__ == "__main__":
-    if len(sys.argv) > 1:
-        # Process specific file
-        tif_file = sys.argv[1]
-        generate_png_preview(tif_file)
-    else:
-        # Process all files in predictions directory
-        generate_all_previews()

inspect_model_odc.py

@@ -1,64 +0,0 @@
-"""
-Inspect model_odc.joblib to see what it actually contains
-"""
-
-import joblib
-from pathlib import Path
-
-model_path = Path("model_train/model_odc.joblib")
-
-if model_path.exists():
-    print("Loading model_odc.joblib...")
-    model_data = joblib.load(model_path)
-    
-    print(f"\nModel type: {type(model_data)}")
-    print(f"Model class: {model_data.__class__.__name__}")
-    
-    # Check if it's a dict
-    if isinstance(model_data, dict):
-        print(f"\nModel is a dict with keys: {model_data.keys()}")
-        model = model_data.get('model')
-    else:
-        model = model_data
-    
-    print(f"\nActual model type: {type(model)}")
-    print(f"Actual model class: {model.__class__.__name__}")
-    
-    # Try to get feature info
-    if hasattr(model, 'n_features_in_'):
-        print(f"\nn_features_in_: {model.n_features_in_}")
-    
-    if hasattr(model, 'feature_names_in_'):
-        print(f"feature_names_in_: {model.feature_names_in_}")
-    
-    # If it's a GridSearchCV
-    if hasattr(model, 'best_estimator_'):
-        print(f"\nThis is a GridSearchCV!")
-        print(f"Best estimator: {model.best_estimator_}")
-        
-        best_est = model.best_estimator_
-        if hasattr(best_est, 'steps'):
-            print(f"\nPipeline steps:")
-            for step_name, step in best_est.steps:
-                print(f"  - {step_name}: {step.__class__.__name__}")
-                if hasattr(step, 'n_features_in_'):
-                    print(f"      n_features_in_: {step.n_features_in_}")
-    
-    # If it's a Pipeline
-    if hasattr(model, 'steps'):
-        print(f"\nThis is a Pipeline!")
-        print(f"Pipeline steps:")
-        for step_name, step in model.steps:
-            print(f"  - {step_name}: {step.__class__.__name__}")
-            if hasattr(step, 'n_features_in_'):
-                print(f"      n_features_in_: {step.n_features_in_}")
-    
-    # Try to get booster for XGBoost
-    try:
-        if hasattr(model, 'get_booster'):
-            print(f"\nXGBoost num_features: {model.get_booster().num_features()}")
-    except:
-        pass
-    
-else:
-    print(f"Model file not found: {model_path}")

model_manager.py

@@ -1,361 +0,0 @@
-"""
-Model Manager - Hệ thống quản lý và vận hành tất cả các loại models
-Hỗ trợ: XGBoost, Random Forest, Decision Tree, SVM, CNN, và các model khác
-"""
-
-import joblib
-import json
-from pathlib import Path
-from typing import Optional, Dict, List, Any, Tuple
-from datetime import datetime
-import numpy as np
-import warnings
-
-# PyTorch for CNN models
-try:
-    import torch
-    PYTORCH_AVAILABLE = True
-except ImportError:
-    PYTORCH_AVAILABLE = False
-
-warnings.filterwarnings('ignore')
-
-
-class ModelManager:
-    """Quản lý tất cả các models: load, save, list, validate"""
-    
-    def __init__(self, models_dir: str = "model_train"):
-        self.models_dir = Path(models_dir)
-        self.models_dir.mkdir(exist_ok=True)
-        self.current_model = None
-        self.current_metadata = None
-        
-    def list_models(self) -> List[Dict[str, Any]]:
-        """
-        Liệt kê tất cả models có sẵn với metadata
-        
-        Returns:
-            List of dicts containing model info
-        """
-        models = []
-        
-        # Tìm tất cả file .joblib
-        for model_file in self.models_dir.glob("*.joblib"):
-            # Skip Zone.Identifier files
-            if "Zone.Identifier" in model_file.name:
-                continue
-                
-            model_info = {
-                "filename": model_file.name,
-                "path": str(model_file),
-                "size_mb": model_file.stat().st_size / (1024 * 1024),
-                "modified": datetime.fromtimestamp(model_file.stat().st_mtime).isoformat(),
-            }
-            
-            # Tìm metadata file tương ứng
-            metadata_file = model_file.with_suffix('.json')
-            if not metadata_file.exists():
-                # Try with _info.json suffix
-                metadata_file = model_file.parent / (model_file.stem + "_info.json")
-            
-            if metadata_file.exists():
-                try:
-                    with open(metadata_file, 'r') as f:
-                        metadata = json.load(f)
-                    model_info["metadata"] = metadata
-                    model_info["has_metadata"] = True
-                    
-                    # Extract key info
-                    model_info["model_type"] = metadata.get("model_type", "unknown")
-                    model_info["features"] = metadata.get("features", [])
-                    model_info["n_features"] = metadata.get("n_features", 0)
-                    model_info["n_classes"] = metadata.get("n_classes", 0)
-                    model_info["test_accuracy"] = metadata.get("test_accuracy", None)
-                    model_info["timestamp"] = metadata.get("timestamp", None)
-                    model_info["data_source"] = metadata.get("data_source", "unknown")
-                    
-                except Exception as e:
-                    model_info["has_metadata"] = False
-                    model_info["metadata_error"] = str(e)
-            else:
-                model_info["has_metadata"] = False
-            
-            models.append(model_info)
-        
-        # Sort by modified time (newest first)
-        models.sort(key=lambda x: x["modified"], reverse=True)
-        
-        return models
-    
-    def load_model(self, model_filename: str) -> Tuple[Any, Optional[Any], Dict[str, Any]]:
-        """
-        Load model từ file
-        
-        Args:
-            model_filename: Tên file model (ví dụ: "model_odc.joblib")
-            
-        Returns:
-            Tuple of (model, label_encoder, metadata)
-        """
-        model_path = self.models_dir / model_filename
-        
-        if not model_path.exists():
-            raise FileNotFoundError(f"Model không tồn tại: {model_filename}")
-        
-        # Load model
-        print(f"[MODEL MANAGER] Loading model: {model_filename}")
-        model_data = joblib.load(model_path)
-        
-        # Extract model and encoder
-        if isinstance(model_data, dict):
-            model = model_data.get('model')
-            label_encoder = model_data.get('label_encoder')
-        else:
-            # Old format: model only
-            model = model_data
-            label_encoder = None
-        
-        # Load metadata
-        metadata = self._load_metadata(model_filename)
-        
-        # Store current model
-        self.current_model = model
-        self.current_metadata = metadata
-        
-        # Check if CNN model and set to eval mode
-        if PYTORCH_AVAILABLE and hasattr(model, '__class__') and 'CNN' in model.__class__.__name__:
-            model.eval()
-            print(f"[MODEL MANAGER] PyTorch CNN model detected and set to eval mode")
-        
-        print(f"[MODEL MANAGER] Model loaded successfully")
-        print(f"  - Type: {metadata.get('model_type', 'unknown')}")
-        print(f"  - Features: {metadata.get('n_features', 'N/A')}")
-        print(f"  - Classes: {metadata.get('n_classes', 'N/A')}")
-        print(f"  - Accuracy: {metadata.get('test_accuracy', 'N/A')}")
-        
-        return model, label_encoder, metadata
-    
-    def _load_metadata(self, model_filename: str) -> Dict[str, Any]:
-        """Load metadata cho model"""
-        model_path = self.models_dir / model_filename
-        
-        # Try multiple metadata file patterns
-        metadata_files = [
-            model_path.with_suffix('.json'),
-            model_path.parent / (model_path.stem + "_info.json"),
-        ]
-        
-        for metadata_file in metadata_files:
-            if metadata_file.exists():
-                try:
-                    with open(metadata_file, 'r') as f:
-                        return json.load(f)
-                except Exception as e:
-                    print(f"[MODEL MANAGER] Warning: Could not load metadata from {metadata_file}: {e}")
-        
-        # Return default metadata if not found
-        print(f"[MODEL MANAGER] Warning: No metadata found for {model_filename}")
-        return {
-            "model_type": "unknown",
-            "features": [],
-            "n_features": 0,
-            "n_classes": 0,
-            "timestamp": None
-        }
-    
-    def save_model(self, model: Any, metadata: Dict[str, Any], 
-                   model_filename: Optional[str] = None,
-                   label_encoder: Optional[Any] = None) -> str:
-        """
-        Save model với metadata
-        
-        Args:
-            model: Model object
-            metadata: Dict chứa thông tin về model
-            model_filename: Tên file (optional, sẽ auto-generate nếu không có)
-            label_encoder: Label encoder (optional)
-            
-        Returns:
-            Path to saved model file
-        """
-        # Generate filename if not provided
-        if model_filename is None:
-            model_type = metadata.get("model_type", "model")
-            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-            model_filename = f"model_{model_type}_{timestamp}.joblib"
-        
-        model_path = self.models_dir / model_filename
-        metadata_path = model_path.parent / (model_path.stem + "_info.json")
-        
-        # Prepare model data
-        if label_encoder is not None:
-            model_data = {
-                'model': model,
-                'label_encoder': label_encoder
-            }
-        else:
-            model_data = {
-                'model': model
-            }
-        
-        # Save model
-        print(f"[MODEL MANAGER] Saving model to: {model_path}")
-        joblib.dump(model_data, model_path)
-        
-        # Save metadata
-        print(f"[MODEL MANAGER] Saving metadata to: {metadata_path}")
-        with open(metadata_path, 'w') as f:
-            json.dump(metadata, f, indent=2)
-        
-        print(f"[MODEL MANAGER] Model saved successfully!")
-        
-        return str(model_path)
-    
-    def validate_model(self, model_filename: str) -> Dict[str, Any]:
-        """
-        Validate model file và kiểm tra integrity
-        
-        Returns:
-            Dict with validation results
-        """
-        result = {
-            "valid": False,
-            "errors": [],
-            "warnings": []
-        }
-        
-        model_path = self.models_dir / model_filename
-        
-        # Check file exists
-        if not model_path.exists():
-            result["errors"].append(f"File không tồn tại: {model_filename}")
-            return result
-        
-        # Try to load model
-        try:
-            model, encoder, metadata = self.load_model(model_filename)
-            result["valid"] = True
-            
-            # Check metadata
-            if not metadata or metadata.get("model_type") == "unknown":
-                result["warnings"].append("Không có metadata hoặc metadata không đầy đủ")
-            
-            # Check required features
-            if not metadata.get("features"):
-                result["warnings"].append("Danh sách features không có trong metadata")
-            
-            # Check model object
-            if model is None:
-                result["errors"].append("Model object is None")
-                result["valid"] = False
-            
-        except Exception as e:
-            result["errors"].append(f"Lỗi khi load model: {str(e)}")
-            result["valid"] = False
-        
-        return result
-    
-    def get_required_features(self, model_filename: str) -> List[str]:
-        """
-        Lấy danh sách features cần thiết cho model
-        
-        Returns:
-            List of feature names
-        """
-        metadata = self._load_metadata(model_filename)
-        return metadata.get("features", [])
-    
-    def predict(self, model_filename: str, X: np.ndarray) -> np.ndarray:
-        """
-        Predict using specified model
-        
-        Args:
-            model_filename: Model file name
-            X: Features array (n_samples, n_features)
-            
-        Returns:
-            Predictions array
-        """
-        if self.current_model is None or model_filename != getattr(self, '_current_model_filename', None):
-            model, encoder, metadata = self.load_model(model_filename)
-            self._current_model_filename = model_filename
-        else:
-            model = self.current_model
-            metadata = self.current_metadata
-        
-        # Validate input features
-        expected_features = metadata.get("n_features", 0)
-        if X.shape[1] != expected_features:
-            raise ValueError(f"Expected {expected_features} features, got {X.shape[1]}")
-        
-        # Predict
-        predictions = model.predict(X)
-        
-        return predictions
-    
-    def get_model_info(self, model_filename: str) -> Dict[str, Any]:
-        """Get detailed info about a model"""
-        models = self.list_models()
-        for model in models:
-            if model["filename"] == model_filename:
-                return model
-        return None
-    
-    def delete_model(self, model_filename: str) -> bool:
-        """
-        Xóa model và metadata
-        
-        Returns:
-            True if successful
-        """
-        model_path = self.models_dir / model_filename
-        
-        if not model_path.exists():
-            return False
-        
-        # Delete model file
-        model_path.unlink()
-        
-        # Delete metadata file if exists
-        metadata_file = model_path.with_suffix('.json')
-        if metadata_file.exists():
-            metadata_file.unlink()
-        
-        # Try alternative metadata file name
-        metadata_file_alt = model_path.parent / (model_path.stem + "_info.json")
-        if metadata_file_alt.exists():
-            metadata_file_alt.unlink()
-        
-        return True
-    
-    def get_latest_model(self, model_type: Optional[str] = None) -> Optional[str]:
-        """
-        Lấy model mới nhất (theo thời gian modified)
-        
-        Args:
-            model_type: Filter by model type (xgboost, cnn, etc.), None for any
-            
-        Returns:
-            Model filename or None
-        """
-        models = self.list_models()
-        
-        if model_type:
-            models = [m for m in models if m.get("model_type") == model_type]
-        
-        if not models:
-            return None
-        
-        # Already sorted by modified time
-        return models[0]["filename"]
-
-
-# Singleton instance
-_model_manager = None
-
-def get_model_manager() -> ModelManager:
-    """Get singleton ModelManager instance"""
-    global _model_manager
-    if _model_manager is None:
-        _model_manager = ModelManager()
-    return _model_manager

model_train/model.joblib

@@ -0,0 +1,3 @@
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+size 14112695

model_train/model_cnn_20251214_180423.joblib

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+size 41536

model_train/model_cnn_20251214_180423_info.json

@@ -0,0 +1,33 @@
+{
+  "timestamp": "2025-12-14T18:04:27.406540",
+  "data_source": "Microsoft Planetary Computer STAC",
+  "collections": [
+    "sentinel-2-l2a",
+    "sentinel-1-rtc"
+  ],
+  "features": [
+    "NDVI_mean",
+    "VH_dB_mean",
+    "VV_dB_mean"
+  ],
+  "training_samples": 510,
+  "testing_samples": 128,
+  "train_accuracy": 0.515686274509804,
+  "test_accuracy": 0.5,
+  "model_type": "cnn",
+  "device": "cpu",
+  "n_estimators": 50,
+  "max_depth": null,
+  "learning_rate": null,
+  "cnn_epochs": 25,
+  "n_features": 3,
+  "n_classes": 7,
+  "bbox": [
+    105.6,
+    9.3,
+    106.2,
+    9.8
+  ],
+  "time_range": "2023-03-01/2023-05-31",
+  "resolution": 20
+}

model_train/model_cnn_20251214_181104.joblib

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+size 41536

model_train/model_cnn_20251214_181104_info.json

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+{
+  "timestamp": "2025-12-14T18:17:48.399298",
+  "data_source": "Microsoft Planetary Computer STAC",
+  "collections": [
+    "sentinel-2-l2a",
+    "sentinel-1-rtc"
+  ],
+  "features": [
+    "NDVI_mean",
+    "VH_dB_mean",
+    "VV_dB_mean"
+  ],
+  "training_samples": 510,
+  "testing_samples": 128,
+  "train_accuracy": 0.4803921568627451,
+  "test_accuracy": 0.484375,
+  "model_type": "cnn",
+  "device": "cpu",
+  "n_estimators": 50,
+  "max_depth": null,
+  "learning_rate": null,
+  "cnn_epochs": 25,
+  "n_features": 3,
+  "n_classes": 7,
+  "bbox": [
+    105.6,
+    9.3,
+    106.2,
+    9.8
+  ],
+  "time_range": "2023-03-01/2023-05-25",
+  "resolution": 20
+}

model_train/model_cnn_20251214_182307.joblib

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+size 41536

model_train/model_cnn_20251214_182307_info.json

@@ -0,0 +1,33 @@
+{
+  "timestamp": "2025-12-14T18:23:10.713912",
+  "data_source": "Microsoft Planetary Computer STAC",
+  "collections": [
+    "sentinel-2-l2a",
+    "sentinel-1-rtc"
+  ],
+  "features": [
+    "NDVI_mean",
+    "VH_dB_mean",
+    "VV_dB_mean"
+  ],
+  "training_samples": 510,
+  "testing_samples": 128,
+  "train_accuracy": 0.46862745098039216,
+  "test_accuracy": 0.4609375,
+  "model_type": "cnn",
+  "device": "cpu",
+  "n_estimators": 50,
+  "max_depth": null,
+  "learning_rate": null,
+  "cnn_epochs": 25,
+  "n_features": 3,
+  "n_classes": 7,
+  "bbox": [
+    105.6,
+    9.3,
+    106.2,
+    9.8
+  ],
+  "time_range": "2023-03-01/2023-05-31",
+  "resolution": 20
+}

model_train/model_new.joblib

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+size 11474743

model_train/model_xgboost_gpu_20251212_125754.joblib

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+size 1347520

model_train/model_xgboost_gpu_20251212_125754_info.json

@@ -0,0 +1,24 @@
+{
+  "timestamp": "2025-12-12T12:57:54.509336",
+  "data_source": "Microsoft Planetary Computer STAC",
+  "collections": [
+    "sentinel-2-l2a",
+    "sentinel-1-rtc"
+  ],
+  "features": [
+    "NDVI_mean",
+    "VH_dB_mean",
+    "VV_dB_mean"
+  ],
+  "training_samples": 510,
+  "testing_samples": 128,
+  "train_accuracy": 1.0,
+  "test_accuracy": 0.578125,
+  "model_type": "XGBClassifier",
+  "device": "cuda:0",
+  "gpu_device": "RTX 4060",
+  "tree_method": "hist",
+  "n_estimators": 100,
+  "max_depth": 20,
+  "learning_rate": 0.1
+}

model_train/model_xgboost_gpu_20251212_220850.joblib

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+size 1347520

model_train/model_xgboost_gpu_20251212_220850_info.json

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+{
+  "timestamp": "2025-12-12T22:15:25.794614",
+  "data_source": "Microsoft Planetary Computer STAC",
+  "collections": [
+    "sentinel-2-l2a",
+    "sentinel-1-rtc"
+  ],
+  "features": [
+    "NDVI_mean",
+    "VH_dB_mean",
+    "VV_dB_mean"
+  ],
+  "training_samples": 510,
+  "testing_samples": 128,
+  "train_accuracy": 1.0,
+  "test_accuracy": 0.578125,
+  "model_type": "XGBClassifier",
+  "device": "cuda:0",
+  "tree_method": "hist",
+  "n_estimators": 100,
+  "max_depth": 20,
+  "learning_rate": 0.1,
+  "bbox": [
+    105.6,
+    9.3,
+    106.2,
+    9.8
+  ],
+  "time_range": "2023-03-01/2023-05-31",
+  "resolution": 20
+}

model_train/model_xgboost_gpu_20251212_223236.joblib

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+size 556184

model_train/model_xgboost_gpu_20251212_223236_info.json

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+  "timestamp": "2025-12-12T22:33:42.950629",
+  "data_source": "Microsoft Planetary Computer STAC",
+  "collections": [
+    "sentinel-2-l2a",
+    "sentinel-1-rtc"
+  ],
+  "features": [
+    "NDVI_mean",
+    "VH_dB_mean",
+    "VV_dB_mean"
+  ],
+  "training_samples": 904,
+  "testing_samples": 226,
+  "train_accuracy": 0.19911504424778761,
+  "test_accuracy": 0.19911504424778761,
+  "model_type": "XGBClassifier",
+  "device": "cuda:0",
+  "tree_method": "hist",
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+  "learning_rate": 0.1,
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+    10.944563,
+    104.972717,
+    11.016689
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+  "resolution": 20
+}

model_train/model_xgboost_gpu_20251214_133256.joblib

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+size 1347520

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+  "timestamp": "2025-12-14T13:40:03.325930",
+  "data_source": "Microsoft Planetary Computer STAC",
+  "collections": [
+    "sentinel-2-l2a",
+    "sentinel-1-rtc"
+  ],
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+    "NDVI_mean",
+    "VH_dB_mean",
+    "VV_dB_mean"
+  ],
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+  "testing_samples": 128,
+  "train_accuracy": 1.0,
+  "test_accuracy": 0.578125,
+  "model_type": "XGBClassifier",
+  "device": "cuda:0",
+  "tree_method": "hist",
+  "n_estimators": 100,
+  "max_depth": 20,
+  "learning_rate": 0.1,
+  "bbox": [
+    105.6,
+    9.3,
+    106.2,
+    9.8
+  ],
+  "time_range": "2023-03-01/2023-05-31",
+  "resolution": 20
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+size 2088632

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+  "timestamp": "2025-12-14T16:52:19.862770",
+  "data_source": "Microsoft Planetary Computer STAC",
+  "collections": [
+    "sentinel-2-l2a",
+    "sentinel-1-rtc"
+  ],
+  "features": [
+    "NDVI_mean",
+    "VH_dB_mean",
+    "VV_dB_mean"
+  ],
+  "training_samples": 859,
+  "testing_samples": 215,
+  "train_accuracy": 0.9976717112922002,
+  "test_accuracy": 0.6837209302325581,
+  "model_type": "XGBClassifier",
+  "device": "cuda:0",
+  "tree_method": "hist",
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+  "learning_rate": 0.1,
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+    105.6,
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+    106.2,
+    9.8
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+  "resolution": 20
+}

new_import_ODC.py

@@ -224,46 +224,12 @@ def train_with_rf(X_train, X_val, y_train, y_val):
     return grid_search
 
 
-def save_model(name_file, model, metadata=None, label_encoder=None):
-    """
-    Save model với metadata để tương thích với ModelManager
-    
-    Args:
-        name_file: Tên file model
-        model: Model object
-        metadata: Dict chứa thông tin về model (optional)
-        label_encoder: Label encoder (optional)
-    """
-    from model_manager import get_model_manager
-    
+def save_model(name_file, grid_search):
     dir_save_model = "model_train"
     if not os.path.exists(dir_save_model):
         os.mkdir(dir_save_model)
-    
-    # Nếu có metadata, sử dụng ModelManager
-    if metadata is not None:
-        model_manager = get_model_manager()
-        model_manager.save_model(
-            model=model,
-            metadata=metadata,
-            model_filename=name_file,
-            label_encoder=label_encoder
-        )
-    else:
-        # Legacy mode: save trực tiếp (backward compatibility)
-        model_data = {
-            'model': model,
-            'label_encoder': label_encoder
-        } if label_encoder is not None else model
-        
-        joblib.dump(model_data, os.path.join(dir_save_model, name_file))
-    
-    print(f"✅ Model saved: {name_file}")
-    if metadata:
-        print(f"   - Type: {metadata.get('model_type', 'N/A')}")
-        print(f"   - Features: {metadata.get('n_features', 'N/A')}")
-        print(f"   - Accuracy: {metadata.get('test_accuracy', 'N/A')}")
-
+    joblib.dump(grid_search, os.path.join(dir_save_model, name_file))
+    print("Done!")
     
     
 def predict(model, data_crs, ndvi, vh, vv):

predictions/prediction_20251212_230423.tif

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