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hoàn thành chức năng phân lô trên ảnh predict
update requirement.txt
2026-03-27 09:18:38 +07:00 · 2026-02-18 16:22:18 +07:00 · 2026-02-17 22:13:35 +07:00
14 changed files with 1963 additions and 180 deletions
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+# 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
+
@@ -224,6 +224,9 @@ class PredictionConfig(BaseModel):
    # Cloud removal strategy
    cloud_removal_method: str = "classic"
    cloud_removal_model: Optional[str] = None  # Optional: .pth model filename for deep learning cloud removal
+    
+    # Shapefile overlay for visualization
+    shapefile_overlay: Optional[str] = None  # Path to shapefile for overlaying boundaries


 class TrainingStatus(BaseModel):
@@ -286,6 +289,7 @@ class PredictionWithNDVIConfig(BaseModel):
    export_classification: bool = True  # Export classification raster
    cloud_removal_method: str = "classic"
    cloud_removal_model: Optional[str] = None  # Optional: .pth model filename for deep learning cloud removal
+    shapefile_overlay: Optional[str] = None  # Path to shapefile for overlaying boundaries


 class CloudRemovalTrainingConfig(BaseModel):
@@ -1228,6 +1232,87 @@ async def list_training_files():
    }


+@app.get("/api/overlay/shapefiles")
+async def list_overlay_shapefiles():
+    """Liệt kê các shapefile có sẵn cho overlay trên prediction"""
+    overlay_dirs = ["region", "ChauThanh", "ThuanHoa"]
+    shapefiles = []
+    
+    for overlay_dir in overlay_dirs:
+        dir_path = Path(overlay_dir)
+        if not dir_path.exists():
+            continue
+            
+        # Find all .shp files in this directory and subdirectories
+        for shp_file in dir_path.rglob("*.shp"):
+            try:
+                file_size = shp_file.stat().st_size
+                file_modified = datetime.fromtimestamp(shp_file.stat().st_mtime).isoformat()
+                
+                # Try to read shapefile to get feature count and bbox
+                import geopandas as gpd
+                gdf = gpd.read_file(str(shp_file))
+                feature_count = len(gdf)
+                
+                # Calculate bbox (always in EPSG:4326 for consistency)
+                bbox = None
+                if not gdf.empty and gdf.crs:
+                    try:
+                        # Reproject to EPSG:4326 if needed
+                        if gdf.crs != "EPSG:4326":
+                            gdf_4326 = gdf.to_crs("EPSG:4326")
+                        else:
+                            gdf_4326 = gdf
+                        
+                        # Get total bounds [minx, miny, maxx, maxy]
+                        bounds = gdf_4326.total_bounds
+                        if len(bounds) == 4:
+                            bbox = [
+                                float(bounds[0]),  # min_lon
+                                float(bounds[1]),  # min_lat
+                                float(bounds[2]),  # max_lon
+                                float(bounds[3])   # max_lat
+                            ]
+                    except Exception as bbox_error:
+                        print(f"[WARNING] Cannot calculate bbox for {shp_file}: {bbox_error}")
+                
+                # Get relative path from workspace root
+                relative_path = str(shp_file)
+                
+                shapefiles.append({
+                    "filename": shp_file.name,
+                    "path": relative_path,
+                    "directory": overlay_dir,
+                    "size_bytes": file_size,
+                    "size_mb": round(file_size / 1024 / 1024, 2),
+                    "modified": file_modified,
+                    "feature_count": feature_count,
+                    "crs": str(gdf.crs) if gdf.crs else "Unknown",
+                    "bbox": bbox  # [min_lon, min_lat, max_lon, max_lat] in EPSG:4326
+                })
+            except Exception as e:
+                # If cannot read shapefile, just add basic info
+                file_size = shp_file.stat().st_size
+                file_modified = datetime.fromtimestamp(shp_file.stat().st_mtime).isoformat()
+                relative_path = str(shp_file)
+                
+                shapefiles.append({
+                    "filename": shp_file.name,
+                    "path": relative_path,
+                    "directory": overlay_dir,
+                    "size_bytes": file_size,
+                    "size_mb": round(file_size / 1024 / 1024, 2),
+                    "modified": file_modified,
+                    "error": f"Cannot read shapefile: {str(e)}"
+                })
+    
+    return {
+        "shapefiles": shapefiles,
+        "count": len(shapefiles),
+        "directories": overlay_dirs
+    }
+
+
@app.get("/api/training/shapefile/{filename}/labels")
 async def get_shapefile_labels(filename: str):
    """Lấy các label từ một shapefile cụ thể"""
@@ -1729,6 +1814,85 @@ def update_progress(message: str):
    print(f"[PROGRESS] {message}")


+def rasterize_shapefile_overlay(shapefile_path, reference_raster, boundary_value=255):
+    """
+    Rasterize shapefile boundaries to overlay on prediction result.
+    
+    Args:
+        shapefile_path: Path to shapefile
+        reference_raster: xarray DataArray to match dimensions and CRS
+        boundary_value: Value to use for boundaries (default 255 for white)
+    
+    Returns:
+        numpy array with boundaries, same shape as reference_raster
+    """
+    try:
+        import geopandas as gpd
+        from rasterio.features import rasterize
+        import numpy as np
+        
+        # Read shapefile
+        gdf = gpd.read_file(shapefile_path)
+        print(f"[OVERLAY] Loaded shapefile with {len(gdf)} features, CRS: {gdf.crs}")
+        
+        # Ensure CRS matches
+        target_crs = reference_raster.rio.crs
+        if gdf.crs != target_crs:
+            print(f"[OVERLAY] Reprojecting from {gdf.crs} to {target_crs}")
+            gdf = gdf.to_crs(target_crs)
+        
+        # Get raster dimensions and transform
+        height, width = reference_raster.shape
+        transform = reference_raster.rio.transform()
+        print(f"[OVERLAY] Raster dimensions: {height}x{width}")
+        print(f"[OVERLAY] Transform: {transform}")
+        
+        # Calculate appropriate buffer size based on pixel resolution
+        # Get pixel size from transform (transform[0] is x resolution)
+        pixel_size = abs(transform[0])  # in CRS units
+        # Very thin boundary - only 0.2 pixels wide for 1px line
+        buffer_distance = pixel_size * 0.2
+        print(f"[OVERLAY] Pixel size: {pixel_size}, Buffer distance: {buffer_distance} (thin 1px line)")
+        
+        # Create boundary geometries with minimal buffering
+        boundary_geoms = []
+        for idx, geom in enumerate(gdf.geometry):
+            if geom is not None and geom.is_valid:
+                # Get boundary of each polygon
+                boundary = geom.boundary
+                if boundary is not None:
+                    # Minimal buffer for 1-pixel thin line
+                    buffered = boundary.buffer(buffer_distance)
+                    boundary_geoms.append((buffered, boundary_value))
+        
+        if not boundary_geoms:
+            print(f"[WARNING] No valid boundary geometries found in {shapefile_path}")
+            return np.zeros((height, width), dtype=np.uint8)
+        
+        print(f"[OVERLAY] Rasterizing {len(boundary_geoms)} boundaries...")
+        
+        # Rasterize boundaries
+        boundary_mask = rasterize(
+            shapes=boundary_geoms,
+            out_shape=(height, width),
+            transform=transform,
+            fill=0,  # Background
+            dtype=np.uint8
+        )
+        
+        boundary_count = np.count_nonzero(boundary_mask)
+        print(f"[OVERLAY] Boundary pixels: {boundary_count} / {height*width} ({boundary_count/(height*width)*100:.2f}%)")
+        
+        if boundary_count == 0:
+            print(f"[OVERLAY WARNING] No boundary pixels were rasterized! Check CRS and geometry overlap.")
+        
+        return boundary_mask
+        
+    except Exception as e:
+        print(f"[ERROR] Failed to rasterize shapefile {shapefile_path}: {e}")
+        return None
+
+
 def update_prediction_progress(message: str):
    """Cập nhật prediction progress message"""
    global prediction_status
@@ -2083,6 +2247,33 @@ async def run_prediction(config: PredictionConfig):
        
        prediction_da.rio.to_raster(str(output_file), driver="GTiff")
        
+        # ============ SHAPEFILE OVERLAY ============
+        overlay_mask = None
+        if config.shapefile_overlay:
+            prediction_status["progress"] = f"Đang overlay shapefile: {config.shapefile_overlay}..."
+            print(f"[OVERLAY] Shapefile overlay requested: {config.shapefile_overlay}")
+            
+            # Validate shapefile path exists
+            shapefile_path = Path(config.shapefile_overlay)
+            if not shapefile_path.exists():
+                print(f"[OVERLAY ERROR] Shapefile not found: {shapefile_path}")
+                print(f"[OVERLAY ERROR] Absolute path: {shapefile_path.absolute()}")
+                print(f"[OVERLAY ERROR] Current working directory: {Path.cwd()}")
+            else:
+                print(f"[OVERLAY] Shapefile exists: {shapefile_path.absolute()}")
+                try:
+                    overlay_mask = rasterize_shapefile_overlay(str(shapefile_path), prediction_da)
+                    if overlay_mask is not None and np.count_nonzero(overlay_mask) > 0:
+                        print(f"[OVERLAY] Successfully rasterized shapefile boundaries ({np.count_nonzero(overlay_mask)} pixels)")
+                    else:
+                        print(f"[OVERLAY WARNING] Shapefile rasterized but no boundary pixels found")
+                except Exception as overlay_error:
+                    print(f"[OVERLAY ERROR] Exception: {overlay_error}")
+                    import traceback
+                    traceback.print_exc()
+        else:
+            print(f"[OVERLAY] No shapefile overlay requested")
+        
        # Generate PNG preview for web display
        prediction_status["progress"] = "Đang tạo PNG preview..."
        png_file = output_dir / f"prediction_{timestamp}.png"
@@ -2097,6 +2288,42 @@ async def run_prediction(config: PredictionConfig):
            
            # Plot prediction with colormap
            im = ax.imshow(predictions_2d, cmap='tab20', interpolation='nearest')
+            
+            # Overlay shapefile boundaries if available
+            if overlay_mask is not None and np.count_nonzero(overlay_mask) > 0:
+                print(f"[PNG OVERLAY] Overlaying {np.count_nonzero(overlay_mask)} boundary pixels")
+                
+                # Create a mask for boundaries (where overlay_mask > 0)
+                boundary_mask = overlay_mask > 0
+                
+                # Method: Direct overlay with high-contrast colors
+                # Create RGBA overlay image
+                overlay_rgba = np.zeros((*predictions_2d.shape, 4))
+                overlay_rgba[boundary_mask, 0] = 1.0  # Red = 1.0 (white)
+                overlay_rgba[boundary_mask, 1] = 1.0  # Green = 1.0 (white)
+                overlay_rgba[boundary_mask, 2] = 1.0  # Blue = 1.0 (white)
+                overlay_rgba[boundary_mask, 3] = 1.0  # Alpha = 1.0 (fully opaque)
+                
+                # Overlay on top of prediction
+                ax.imshow(overlay_rgba, interpolation='nearest')
+                
+                # Also add a black outline for better contrast
+                from scipy import ndimage
+                boundary_dilated = ndimage.binary_dilation(boundary_mask, iterations=1)
+                boundary_outline = boundary_dilated & ~boundary_mask
+                
+                outline_rgba = np.zeros((*predictions_2d.shape, 4))
+                outline_rgba[boundary_outline, 0] = 0.0  # Black outline
+                outline_rgba[boundary_outline, 1] = 0.0
+                outline_rgba[boundary_outline, 2] = 0.0
+                outline_rgba[boundary_outline, 3] = 0.8
+                
+                ax.imshow(outline_rgba, interpolation='nearest')
+                
+                print(f"[PNG OVERLAY] Added shapefile boundaries to visualization (direct overlay method)")
+            else:
+                print(f"[PNG OVERLAY] No overlay mask or empty mask (pixels: {np.count_nonzero(overlay_mask) if overlay_mask is not None else 0})")
+            
            ax.set_title(f'Land Classification - {timestamp}', fontsize=16, fontweight='bold', pad=20)
            ax.set_xlabel('X (pixels)', fontsize=11)
            ax.set_ylabel('Y (pixels)', fontsize=11)
@@ -2176,7 +2403,9 @@ async def run_prediction(config: PredictionConfig):
            "n_features": features.shape[1],
            "feature_mode": feature_mode,
            "used_radar": use_radar,
-            "model_used": config.model_filename
+            "model_used": config.model_filename,
+            "shapefile_overlay": config.shapefile_overlay,
+            "overlay_applied": overlay_mask is not None
        }
        
        # Auto generate prediction report
@@ -4355,8 +4584,69 @@ async def predict_with_ndvi(config: PredictionWithNDVIConfig, background_tasks:
                import matplotlib.pyplot as plt
                from matplotlib.patches import Patch
                
+                # ============ SHAPEFILE OVERLAY ============
+                overlay_mask = None
+                if config.shapefile_overlay:
+                    print(f"[OVERLAY] Shapefile overlay requested: {config.shapefile_overlay}")
+                    
+                    # Validate shapefile path exists
+                    shapefile_path = Path(config.shapefile_overlay)
+                    if not shapefile_path.exists():
+                        print(f"[OVERLAY ERROR] Shapefile not found: {shapefile_path}")
+                        print(f"[OVERLAY ERROR] Absolute path: {shapefile_path.absolute()}")
+                    else:
+                        print(f"[OVERLAY] Shapefile exists: {shapefile_path.absolute()}")
+                        try:
+                            # Import rioxarray for rio accessor
+                            import rioxarray
+                            
+                            # Create temporary DataArray for rasterization
+                            temp_da = xr.DataArray(
+                                prediction_raster,
+                                coords={
+                                    "y": np.linspace(bbox[3], bbox[1], height),
+                                    "x": np.linspace(bbox[0], bbox[2], width)
+                                },
+                                dims=["y", "x"]
+                            )
+                            temp_da.rio.write_crs("EPSG:4326", inplace=True)
+                            temp_da.rio.write_transform(transform, inplace=True)
+                            
+                            overlay_mask = rasterize_shapefile_overlay(str(shapefile_path), temp_da)
+                            if overlay_mask is not None and np.count_nonzero(overlay_mask) > 0:
+                                print(f"[OVERLAY] Successfully rasterized shapefile boundaries ({np.count_nonzero(overlay_mask)} pixels)")
+                            else:
+                                print(f"[OVERLAY WARNING] Shapefile rasterized but no boundary pixels found")
+                        except Exception as overlay_error:
+                            print(f"[OVERLAY ERROR] Exception: {overlay_error}")
+                            import traceback
+                            traceback.print_exc()
+                else:
+                    print(f"[OVERLAY] No shapefile overlay requested")
+                
                fig, ax = plt.subplots(figsize=(14, 10), dpi=150)
                im = ax.imshow(prediction_raster, cmap='tab20', interpolation='nearest')
+                
+                # Overlay shapefile boundaries if available
+                if overlay_mask is not None and np.count_nonzero(overlay_mask) > 0:
+                    print(f"[PNG OVERLAY] Overlaying {np.count_nonzero(overlay_mask)} boundary pixels (1px thin line)")
+                    
+                    # Create a mask for boundaries
+                    boundary_mask = overlay_mask > 0
+                    
+                    # Create RGBA overlay image - thin 1px white line only
+                    overlay_rgba = np.zeros((*prediction_raster.shape, 4))
+                    overlay_rgba[boundary_mask, 0] = 1.0  # White (R=1)
+                    overlay_rgba[boundary_mask, 1] = 1.0  # White (G=1)
+                    overlay_rgba[boundary_mask, 2] = 1.0  # White (B=1)
+                    overlay_rgba[boundary_mask, 3] = 1.0  # Fully opaque
+                    
+                    ax.imshow(overlay_rgba, interpolation='nearest')
+                    
+                    print(f"[PNG OVERLAY] Added thin 1px shapefile boundaries to visualization")
+                else:
+                    print(f"[PNG OVERLAY] No overlay mask or empty mask")
+                
                ax.set_title(f'Land Classification - {timestamp}', fontsize=16, fontweight='bold', pad=20)
                ax.set_xlabel('X (pixels)', fontsize=11)
                ax.set_ylabel('Y (pixels)', fontsize=11)
@@ -774,6 +774,23 @@
                            </div>
                        </label>
                    </div>
+                    
+                    <!-- Shapefile Overlay Option -->
+                    <div class="form-group" style="margin-bottom: 15px;">
+                        <label style="font-weight: 600; color: #c2410c; margin-bottom: 8px; display: block;">
+                            🗺️ Overlay Shapefile (Hiển thị ranh giới lô đất)
+                        </label>
+                        <select id="shapefileOverlay" onchange="onShapefileSelected(event)" style="width: 100%; padding: 10px; border: 2px solid #fdba74; border-radius: 8px; font-size: 14px; background: white;">
+                            <option value="">-- Không overlay --</option>
+                            <!-- Shapefiles will be loaded here -->
+                        </select>
+                        <div style="font-size: 0.85em; color: #9a3412; margin-top: 4px; line-height: 1.4;">
+                            <b>🎯 Tự động cập nhật vùng prediction:</b><br>
+                            ✅ Khi chọn shapefile → <b>Bbox trên bản đồ tự động thay đổi</b> theo vùng shapefile<br>
+                            ✅ <b>CRS sẽ tự động chuyển đổi</b> - không cần lo về EPSG:4326/9209/32648<br>
+                            💡 Không chọn shapefile → Dùng bbox tùy chỉnh do bạn vẽ trên bản đồ
+                        </div>
+                    </div>
                </div>
                
                <button class="btn btn-primary" onclick="startPrediction()" id="predictBtn" style="margin-top: 5px; width: 100%; font-size: 1.1em; padding: 16px;">
@@ -1651,6 +1668,29 @@
                }
            }
            
+            // Get shapefile overlay option
+            const shapefileOverlay = document.getElementById('shapefileOverlay').value;
+            
+            // Warn if no shapefile selected (optional but recommended)
+            if (!shapefileOverlay) {
+                const confirmWithoutShapefile = confirm(
+                    '⚠️ CẢNH BÁO: Bạn chưa chọn shapefile!\n\n' +
+                    '❌ Kết quả sẽ KHÔNG có đường ranh giới lô đất.\n\n' +
+                    '💡 Để có đường phân lô trên ảnh kết quả:\n' +
+                    '   - Hủy bỏ\n' +
+                    '   - Chọn shapefile trong dropdown "Overlay Shapefile"\n' +
+                    '   - Chạy lại prediction\n\n' +
+                    'Bạn có muốn tiếp tục KHÔNG CÓ ranh giới lô đất không?'
+                );
+                
+                if (!confirmWithoutShapefile) {
+                    console.log('[PREDICTION] User cancelled to select shapefile');
+                    return;
+                }
+            } else {
+                console.log(`[PREDICTION] Shapefile selected: ${shapefileOverlay}`);
+            }
+            
            const config = {
                model_filename: modelFilename,
                min_lon: selectedBbox.min_lon,
@@ -1666,7 +1706,8 @@
                export_ndvi: exportNDVI,
                export_classification: true,
                cloud_removal_method: cloudRemovalConfig.method,
-                cloud_removal_model: cloudRemovalConfig.model_filename || null
+                cloud_removal_model: cloudRemovalConfig.model_filename || null,
+                shapefile_overlay: shapefileOverlay || null
            };
            
            try {
@@ -2598,6 +2639,7 @@
            loadPredProvinces();  // Load provinces list
            loadNDVIProvinces();  // Load NDVI provinces list
            loadNDVIModels();  // Load models for NDVI
+            loadOverlayShapefiles();  // Load shapefiles for overlay
            
            // Add event listener for model selection
            document.getElementById('modelSelect').addEventListener('change', updateModelInfo);
@@ -2993,6 +3035,153 @@
            
            alert(`✅ Đã áp dụng preset: ${config.name}\n\nBbox: [${config.bbox.join(', ')}]\nThời gian: ${config.start_date} → ${config.end_date}\nSample points: ${config.sample_points}`);
        }
+        
+        // Load overlay shapefiles
+        async function loadOverlayShapefiles() {
+            try {
+                const response = await fetch('/api/overlay/shapefiles');
+                const data = await response.json();
+                
+                const select = document.getElementById('shapefileOverlay');
+                select.innerHTML = '<option value="">-- Không overlay --</option>';
+                
+                if (data.shapefiles && data.shapefiles.length > 0) {
+                    data.shapefiles.forEach(shp => {
+                        const option = document.createElement('option');
+                        option.value = shp.path;
+                        
+                        // Build detailed label with CRS and bbox info
+                        let label = `${shp.filename} - ${shp.feature_count} features`;
+                        
+                        // Add CRS info (important for matching!)
+                        if (shp.crs) {
+                            const crsCode = shp.crs.split(':').pop(); // Extract code from "EPSG:4326"
+                            label += ` | CRS: ${crsCode}`;
+                        }
+                        
+                        // Add bbox info for easy matching with prediction area
+                        if (shp.bbox && shp.bbox.length === 4) {
+                            const [minLon, minLat, maxLon, maxLat] = shp.bbox;
+                            label += ` | Vùng: [${minLon.toFixed(2)}, ${minLat.toFixed(2)}, ${maxLon.toFixed(2)}, ${maxLat.toFixed(2)}]`;
+                        }
+                        
+                        option.textContent = label;
+                        
+                        // Store full shapefile info as data attributes for later use
+                        option.dataset.crs = shp.crs || '';
+                        option.dataset.bbox = JSON.stringify(shp.bbox || []);
+                        option.dataset.featureCount = shp.feature_count;
+                        
+                        select.appendChild(option);
+                    });
+                    
+                    console.log(`[Overlay Shapefiles] Loaded ${data.shapefiles.length} shapefiles`);
+                } else {
+                    console.log('[Overlay Shapefiles] No shapefiles found');
+                }
+                
+                // Add event listener for shapefile selection change (OUTSIDE the if block)
+                // Remove old listener first to prevent duplicates
+                select.removeEventListener('change', onShapefileSelected);
+                select.addEventListener('change', onShapefileSelected);
+                console.log('[Overlay Shapefiles] Event listener attached');
+                
+            } catch (error) {
+                console.error('[Overlay Shapefiles] Error loading shapefiles:', error);
+                const select = document.getElementById('shapefileOverlay');
+                select.innerHTML = '<option value="">Error loading shapefiles</option>';
+            }
+        }
+        
+        // Handle shapefile selection - auto update bbox on map
+        function onShapefileSelected(event) {
+            console.log('[Shapefile Select] Event triggered');
+            console.log('[Shapefile Select] map exists:', typeof map !== 'undefined');
+            console.log('[Shapefile Select] drawnItems exists:', typeof drawnItems !== 'undefined');
+            
+            const selectedOption = event.target.selectedOptions[0];
+            
+            // If no shapefile selected (empty value), keep current bbox
+            if (!selectedOption || !selectedOption.value) {
+                console.log('[Shapefile Select] No shapefile selected, keeping current bbox');
+                return;
+            }
+            
+            console.log('[Shapefile Select] Selected shapefile:', selectedOption.value);
+            
+            // Get bbox from data attribute
+            const bboxData = selectedOption.dataset.bbox;
+            console.log('[Shapefile Select] Bbox data:', bboxData);
+            
+            if (!bboxData || bboxData === '[]') {
+                console.warn('[Shapefile Select] Selected shapefile has no bbox data');
+                return;
+            }
+            
+            try {
+                const bbox = JSON.parse(bboxData);
+                console.log('[Shapefile Select] Parsed bbox:', bbox);
+                
+                if (bbox.length !== 4) {
+                    console.warn('[Shapefile Select] Invalid bbox format:', bbox);
+                    return;
+                }
+                
+                const [minLon, minLat, maxLon, maxLat] = bbox;
+                
+                // Validate bbox
+                if (minLon < -180 || maxLon > 180 || minLat < -90 || maxLat > 90) {
+                    alert('❌ Bbox của shapefile không hợp lệ!');
+                    return;
+                }
+                
+                console.log('[Shapefile Select] Creating rectangle with bounds:', [[minLat, minLon], [maxLat, maxLon]]);
+                
+                // Update prediction map bbox
+                const bounds = [
+                    [minLat, minLon],
+                    [maxLat, maxLon]
+                ];
+                
+                const rectangle = L.rectangle(bounds, {
+                    color: '#667eea',
+                    weight: 3,
+                    fillOpacity: 0.2
+                });
+                
+                // Clear old bbox and add new one
+                console.log('[Shapefile Select] Clearing old layers...');
+                drawnItems.clearLayers();
+                console.log('[Shapefile Select] Adding new rectangle...');
+                drawnItems.addLayer(rectangle);
+                console.log('[Shapefile Select] Fitting map to bounds...');
+                map.fitBounds(bounds, { padding: [50, 50] });
+                
+                // Update selected bbox variable
+                selectedBbox = {
+                    min_lon: minLon,
+                    min_lat: minLat,
+                    max_lon: maxLon,
+                    max_lat: maxLat
+                };
+                
+                // Save to localStorage
+                localStorage.setItem('prediction_bbox', JSON.stringify(selectedBbox));
+                
+                console.log(`[Shapefile Select] Auto-updated bbox from shapefile:`, selectedBbox);
+                
+                // Show notification
+                const crs = selectedOption.dataset.crs || 'Unknown';
+                alert(`✅ Đã tự động cập nhật vùng prediction theo shapefile!\n\n` +
+                      `📍 Bbox: [${minLon.toFixed(4)}, ${minLat.toFixed(4)}, ${maxLon.toFixed(4)}, ${maxLat.toFixed(4)}]\n` +
+                      `🗺️ CRS: ${crs}\n\n` +
+                      `💡 Bạn có thể điều chỉnh lại bằng cách vẽ lại trên bản đồ nếu muốn.`);
+                
+            } catch (e) {
+                console.error('[Shapefile Select] Error parsing bbox:', e);
+                alert(`❌ Lỗi khi xử lý bbox: ${e.message}`);
+            }
+        }
    </script>
 </body>
 </html>
@@ -1,4 +1,3 @@
-affine @ file:///home/conda/feedstock_root/build_artifacts/affine_1733762038348/work
 aiobotocore==2.25.0
 aiohappyeyeballs==2.6.1
 aiohttp==3.12.15
@@ -7,162 +6,63 @@ aiosignal==1.4.0
 alembic==1.16.5
 annotated-doc==0.0.4
 annotated-types==0.7.0
-antimeridian @ file:///home/conda/feedstock_root/build_artifacts/antimeridian_1753706324394/work
-anyio @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_anyio_1758634638/work
-argon2-cffi @ file:///home/conda/feedstock_root/build_artifacts/argon2-cffi_1749017159514/work
-argon2-cffi-bindings @ file:///home/conda/feedstock_root/build_artifacts/argon2-cffi-bindings_1649500328244/work
-arrow @ file:///home/conda/feedstock_root/build_artifacts/arrow_1733584251875/work
 asciitree==0.3.3
-asttokens @ file:///home/conda/feedstock_root/build_artifacts/asttokens_1733250440834/work
-async-lru @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_async-lru_1742153708/work
-async-timeout==3.0.1
-attrs @ file:///home/conda/feedstock_root/build_artifacts/attrs_1741918516150/work
-babel @ file:///home/conda/feedstock_root/build_artifacts/babel_1738490167835/work
-beautifulsoup4 @ file:///home/conda/feedstock_root/build_artifacts/beautifulsoup4_1759146011391/work
-bleach @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_bleach_1737382993/work
 blinker==1.9.0
 bokeh==3.7.3
 boto3==1.40.18
 botocore==1.40.49
-Bottleneck @ file:///croot/bottleneck_1731058641041/work
-branca @ file:///croot/branca_1675157607453/work
-Brotli @ file:///croot/brotli-split_1736182456865/work
-brotlicffi @ file:///croot/brotlicffi_1736182461069/work
-cached-property @ file:///home/conda/feedstock_root/build_artifacts/cached_property_1615209429212/work
 cachetools==6.2.0
 Cartopy==0.25.0
-certifi @ file:///home/conda/feedstock_root/build_artifacts/certifi_1759648874697/work/certifi
-cffi @ file:///croot/cffi_1736182485317/work
-cftime @ file:///home/conda/feedstock_root/build_artifacts/cftime_1649636873066/work
-chardet @ file:///home/conda/feedstock_root/build_artifacts/chardet_1649184137891/work
-charset-normalizer @ file:///croot/charset-normalizer_1721748349566/work
 ciso8601==2.3.3
-click @ file:///home/conda/feedstock_root/build_artifacts/click_1747811314515/work
-click-plugins @ file:///home/conda/feedstock_root/build_artifacts/click-plugins_1750848229740/work
-cligj @ file:///home/conda/feedstock_root/build_artifacts/cligj_1733749956636/work
-cloudpickle @ file:///home/conda/feedstock_root/build_artifacts/cloudpickle_1736947526808/work
 colorama==0.4.6
 colorcet==3.1.0
-comm @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_comm_1753453984/work
-contourpy @ file:///croot/contourpy_1732540045555/work
-cycler @ file:///tmp/build/80754af9/cycler_1637851556182/work
-cytoolz==0.11.2
-dask @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_dask-core_1760473436/work
-dask-gateway @ file:///Users/runner/miniforge3/conda-bld/bld/rattler-build_dask-gateway_1744370153/work/dask-gateway
-dask-glm @ file:///home/conda/feedstock_root/build_artifacts/dask-glm_1701346265909/work
 dask-image==2024.5.3
-dask-ml @ file:///home/conda/feedstock_root/build_artifacts/dask-ml_1679705292494/work
-datacube==1.8.15
+datacube==1.9.4
 datacube_ows==1.9.4
 datashader==0.18.2
 dea-tools==0.3.0
-debugpy @ file:///home/task_175706711740264/conda-bld/debugpy_1757067131873/work
-decorator @ file:///home/conda/feedstock_root/build_artifacts/decorator_1740384970518/work
 deepdiff==8.6.1
-defusedxml @ file:///home/conda/feedstock_root/build_artifacts/defusedxml_1615232257335/work
-deprecat @ file:///home/conda/feedstock_root/build_artifacts/deprecat_1734684036993/work
-distributed @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_distributed_1760476147/work
 eo-tides==0.8.2
-exceptiongroup @ file:///home/conda/feedstock_root/build_artifacts/exceptiongroup_1746947292760/work
-executing @ file:///home/conda/feedstock_root/build_artifacts/executing_1756729339227/work
 fastapi==0.124.3
-fasteners @ file:///home/conda/feedstock_root/build_artifacts/fasteners_1734943108928/work
-fastjsonschema @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_python-fastjsonschema_1755304154/work/dist
 filelock==3.19.1
 fiona==1.10.1
 Flask==3.1.2
 flask-babel==4.0.0
 flatbuffers==25.2.10
 folium==0.20.0
-fonttools @ file:///croot/fonttools_1737039080035/work
-fqdn @ file:///home/conda/feedstock_root/build_artifacts/fqdn_1733327382592/work/dist
 frozenlist==1.7.0
-fsspec @ file:///home/conda/feedstock_root/build_artifacts/fsspec_1756908513222/work
-GDAL @ file:///croot/gdal-split_1734448174900/work/build/swig/python
-GeoAlchemy2 @ file:///home/conda/feedstock_root/build_artifacts/geoalchemy2_1753372953474/work
 geographiclib==2.1
 geojson==3.2.0
 geomad==1.0.0
-geopandas @ file:///croot/geopandas-split_1755761494241/work
 geopy==2.4.1
-greenlet @ file:///home/conda/feedstock_root/build_artifacts/greenlet_1648882383677/work
-h11 @ file:///home/conda/feedstock_root/build_artifacts/h11_1745526374115/work
-h2 @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_h2_1756364871/work
+git-filter-repo==2.47.0
 h3==4.3.1
 hdstats==0.2.1
 holoviews==1.21.0
-hpack @ file:///home/conda/feedstock_root/build_artifacts/hpack_1737618293087/work
-httpcore @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_httpcore_1745602916/work
-httpx @ file:///home/conda/feedstock_root/build_artifacts/httpx_1733663348460/work
 hvplot==0.12.1
-hyperframe @ file:///home/conda/feedstock_root/build_artifacts/hyperframe_1737618333194/work
 idna==3.10
 imagecodecs==2025.3.30
 imageio==2.37.0
-importlib_metadata @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_importlib-metadata_1747934053/work
-ipykernel @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_ipykernel_1760459840/work
 ipyleaflet==0.20.0
-ipython @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_ipython_1748711175/work
 ipywidgets==8.1.7
 iso8601==2.1.0
-isoduration @ file:///home/conda/feedstock_root/build_artifacts/isoduration_1733493628631/work/dist
 itsdangerous==2.2.0
-jedi @ file:///home/conda/feedstock_root/build_artifacts/jedi_1733300866624/work
-Jinja2 @ file:///croot/jinja2_1741710844255/work
-jmespath @ file:///home/conda/feedstock_root/build_artifacts/jmespath_1733229141657/work
-joblib @ file:///home/conda/feedstock_root/build_artifacts/joblib_1756321760188/work
-json5 @ file:///home/conda/feedstock_root/build_artifacts/json5_1755034879854/work
-jsonpointer @ file:///home/conda/feedstock_root/build_artifacts/jsonpointer_1756754132747/work
-jsonschema @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_jsonschema_1755595646/work
 jsonschema-specifications==2025.4.1
-jupyter-events @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_jupyter_events_1738765986/work
 jupyter-leaflet==0.20.0
-jupyter-lsp @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_jupyter-lsp_1756388269/work/jupyter-lsp
 jupyter-ui-poll==1.0.0
-jupyter_client @ file:///home/conda/feedstock_root/build_artifacts/jupyter_client_1733440914442/work
-jupyter_core @ file:///home/conda/feedstock_root/build_artifacts/jupyter_core_1748333051527/work
-jupyter_server @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_jupyter_server_1755870522/work
-jupyter_server_terminals @ file:///home/conda/feedstock_root/build_artifacts/jupyter_server_terminals_1733427956852/work
-jupyterlab @ file:///home/conda/feedstock_root/build_artifacts/jupyterlab_1758913905644/work
-jupyterlab_pygments @ file:///home/conda/feedstock_root/build_artifacts/jupyterlab_pygments_1733328101776/work
-jupyterlab_server @ file:///home/conda/feedstock_root/build_artifacts/jupyterlab_server_1733599573484/work
 jupyterlab_widgets==3.0.15
-kiwisolver @ file:///croot/kiwisolver_1737039087198/work
 lark==1.2.2
 lark-parser==0.12.0
 lazy_loader==0.4
 linkify-it-py==2.0.3
-llvmlite @ file:///croot/llvmlite_1741209858218/work
-locket @ file:///home/conda/feedstock_root/build_artifacts/locket_1650660393415/work
 lxml==5.4.0
-lz4 @ file:///croot/lz4_1736366683208/work
-Mako @ file:///home/conda/feedstock_root/build_artifacts/mako_1744317760971/work
-mapclassify @ file:///croot/mapclassify_1675157730177/work
 Markdown==3.9
 markdown-it-py==4.0.0
-MarkupSafe @ file:///croot/markupsafe_1738584038848/work
 matplotlib==3.10.5
-matplotlib-inline @ file:///home/conda/feedstock_root/build_artifacts/matplotlib-inline_1733416936468/work
 mdit-py-plugins==0.5.0
 mdurl==0.1.2
-mistune @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_mistune_1756495311/work
 mpmath==1.3.0
-msgpack @ file:///home/conda/feedstock_root/build_artifacts/msgpack-python_1648745999384/work
-multidict @ file:///home/conda/feedstock_root/build_artifacts/multidict_1648882415384/work
-multipledispatch @ file:///home/conda/feedstock_root/build_artifacts/multipledispatch_1721907546485/work
 narwhals==2.3.0
-nbclient @ file:///home/conda/feedstock_root/build_artifacts/nbclient_1734628800805/work
-nbconvert @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_nbconvert-core_1738067871/work
-nbformat @ file:///home/conda/feedstock_root/build_artifacts/nbformat_1733402752141/work
-nest_asyncio @ file:///home/conda/feedstock_root/build_artifacts/nest-asyncio_1733325553580/work
-netCDF4 @ file:///croot/netcdf4_1743512888672/work
-networkx @ file:///croot/networkx_1737039604450/work
-notebook @ file:///home/conda/feedstock_root/build_artifacts/notebook_1759152069573/work
-notebook_shim @ file:///home/conda/feedstock_root/build_artifacts/notebook-shim_1733408315203/work
-numba @ file:///croot/numba_1750798165355/work
-numcodecs @ file:///croot/numcodecs_1707513121886/work
-numexpr @ file:///croot/numexpr_1755766469354/work
-numpy @ file:///croot/numpy_and_numpy_base_1755590845055/work/dist/numpy-1.26.4-cp310-cp310-linux_x86_64.whl#sha256=1096d33ad9a9757a1b4b46634d809e894263fc8b78780bff36801684b6e8cc88
 nvidia-cublas-cu12==12.8.4.1
 nvidia-cuda-cupti-cu12==12.8.90
 nvidia-cuda-nvrtc-cu12==12.8.93
@@ -174,136 +74,67 @@ nvidia-curand-cu12==10.3.9.90
 nvidia-cusolver-cu12==11.7.3.90
 nvidia-cusparse-cu12==12.5.8.93
 nvidia-cusparselt-cu12==0.7.1
-nvidia-nccl-cu12==2.27.3
+nvidia-nccl-cu12==2.27.5
 nvidia-nvjitlink-cu12==12.8.93
+nvidia-nvshmem-cu12==3.3.20
 nvidia-nvtx-cu12==12.8.90
 odc-algo==0.2.3
 odc-geo==0.4.10
 odc-io==0.2.2
-odc-loader @ file:///home/conda/feedstock_root/build_artifacts/odc-loader_1743656085024/work
-odc-stac @ file:///home/conda/feedstock_root/build_artifacts/odc-stac_1746136311934/work
 odc-ui==0.2.1
 orderly-set==5.5.0
-overrides @ file:///home/conda/feedstock_root/build_artifacts/overrides_1734587627321/work
 OWSLib==0.34.1
-packaging @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_packaging_1745345660/work
-pandas @ file:///home/task_175982153789305/conda-bld/pandas_1759822248912/work/dist/pandas-2.3.3-cp310-cp310-linux_x86_64.whl#sha256=0de7c83109c411cc2a74419a396c92f65e3d1e457fb4d835e5f100cfb04393a7
-pandocfilters @ file:///home/conda/feedstock_root/build_artifacts/pandocfilters_1631603243851/work
 panel==1.7.5
 param==2.2.1
-parso @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_parso_1755974222/work
-partd @ file:///home/conda/feedstock_root/build_artifacts/partd_1715026491486/work
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 PIMS==0.7
 planetary-computer==1.0.0
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 prometheus_client==0.22.1
 prometheus_flask_exporter==0.23.2
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 propcache==0.3.2
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 pyct==0.5.0
 pydantic==2.11.7
 pydantic_core==2.33.2
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 pyows==0.3.1
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 PyQt6==6.7.1
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 pyshp==2.3.1
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 pystac-client==0.9.0
 python-dateutil==2.9.0.post0
 python-dotenv==1.1.1
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 python-slugify==8.0.4
 pyTMD==2.2.8
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 pyviz_comms==3.0.6
 PyYAML==6.0.2
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 rasterstats==0.20.0
 referencing==0.36.2
 regex==2025.9.1
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 rfc3987==1.3.8
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 ruamel.yaml.clib==0.2.12
 s3fs==2025.9.0
 s3transfer==0.13.1
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 seaborn==0.13.2
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 setuptools-scm==9.2.0
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 six==1.17.0
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 starlette==0.50.0
 sympy==1.14.0
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 text-unidecode==1.3
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 tifffile==2025.5.10
 timescale==0.0.9
 timezonefinder==8.0.0
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 tqdm==4.67.1
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 traittypes==0.2.1
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 typing-inspection==0.4.1
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 uc-micro-py==1.0.3
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 uvicorn==0.38.0
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 Werkzeug==3.1.3
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-xarray @ file:///home/conda/feedstock_root/build_artifacts/xarray_1749743207754/work
 xgboost==3.1.2
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 yarl==1.20.1
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@@ -0,0 +1,313 @@
+affine @ file:///home/conda/feedstock_root/build_artifacts/affine_1733762038348/work
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+pandas @ file:///home/task_175982153789305/conda-bld/pandas_1759822248912/work/dist/pandas-2.3.3-cp310-cp310-linux_x86_64.whl#sha256=0de7c83109c411cc2a74419a396c92f65e3d1e457fb4d835e5f100cfb04393a7
+pandocfilters @ file:///home/conda/feedstock_root/build_artifacts/pandocfilters_1631603243851/work
+panel==1.7.5
+param==2.2.1
+parso @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_parso_1755974222/work
+partd @ file:///home/conda/feedstock_root/build_artifacts/partd_1715026491486/work
+pexpect @ file:///home/conda/feedstock_root/build_artifacts/pexpect_1733301927746/work
+pickleshare @ file:///home/conda/feedstock_root/build_artifacts/pickleshare_1733327343728/work
+pillow @ file:///croot/pillow_1738010226202/work
+PIMS==0.7
+planetary-computer==1.0.0
+platformdirs @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_platformdirs_1756227402/work
+prometheus_client==0.22.1
+prometheus_flask_exporter==0.23.2
+prompt_toolkit @ file:///home/conda/feedstock_root/build_artifacts/prompt-toolkit_1756321756983/work
+propcache==0.3.2
+psutil @ file:///home/conda/feedstock_root/build_artifacts/psutil_1653089181607/work
+psycopg2 @ file:///croot/psycopg2_1744919787325/work
+ptyprocess @ file:///home/conda/feedstock_root/build_artifacts/ptyprocess_1733302279685/work/dist/ptyprocess-0.7.0-py2.py3-none-any.whl#sha256=92c32ff62b5fd8cf325bec5ab90d7be3d2a8ca8c8a3813ff487a8d2002630d1f
+pure_eval @ file:///home/conda/feedstock_root/build_artifacts/pure_eval_1733569405015/work
+pyarrow @ file:///home/task_175983338836370/conda-bld/pyarrow_1759833584228/work/python
+pycparser @ file:///tmp/build/80754af9/pycparser_1636541352034/work
+pyct==0.5.0
+pydantic==2.11.7
+pydantic_core==2.33.2
+Pygments @ file:///home/conda/feedstock_root/build_artifacts/pygments_1750615794071/work
+pyogrio @ file:///croot/pyogrio_1741107161422/work
+pyows==0.3.1
+pyparsing @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_pyparsing_1753873557/work
+pyproj @ file:///croot/pyproj_1739284761968/work
+PyQt6==6.7.1
+PyQt6_sip @ file:///croot/pyqt-split_1753427276959/work/pyqt_sip
+pyshp==2.3.1
+PySocks @ file:///home/builder/ci_310/pysocks_1640793678128/work
+pystac @ file:///home/conda/feedstock_root/build_artifacts/pystac_1758218055393/work
+pystac-client==0.9.0
+python-dateutil==2.9.0.post0
+python-dotenv==1.1.1
+python-json-logger @ file:///home/conda/feedstock_root/build_artifacts/python-json-logger_1677079630776/work
+python-multipart==0.0.21
+python-slugify==8.0.4
+pyTMD==2.2.8
+pytz @ file:///home/conda/feedstock_root/build_artifacts/pytz_1742920838005/work
+pyviz_comms==3.0.6
+PyYAML==6.0.2
+pyzmq @ file:///croot/pyzmq_1734687138743/work
+rasterio @ file:///croot/rasterio_1740069178893/work
+rasterstats==0.20.0
+referencing==0.36.2
+regex==2025.9.1
+requests @ file:///croot/requests_1756709366904/work
+rfc3339_validator @ file:///home/conda/feedstock_root/build_artifacts/rfc3339-validator_1733599910982/work
+rfc3986-validator @ file:///home/conda/feedstock_root/build_artifacts/rfc3986-validator_1598024191506/work
+rfc3987==1.3.8
+rfc3987-syntax @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_rfc3987-syntax_1752876729/work
+rioxarray @ file:///home/conda/feedstock_root/build_artifacts/rioxarray_1737140588464/work
+rpds-py @ file:///croot/rpds-py_1736541261634/work
+ruamel.yaml @ file:///home/conda/feedstock_root/build_artifacts/ruamel.yaml_1649033201098/work
+ruamel.yaml.clib==0.2.12
+s3fs==2025.9.0
+s3transfer==0.13.1
+scikit-image==0.25.2
+scikit-learn==1.7.1
+scipy @ file:///croot/scipy_1747238027288/work/dist/scipy-1.15.3-cp310-cp310-linux_x86_64.whl#sha256=2a791554880ad4f358fcc4cd2a982ffe1e9d472e9241011216b2be797457f1f9
+seaborn==0.13.2
+Send2Trash @ file:///home/conda/feedstock_root/build_artifacts/send2trash_1733322040660/work
+setuptools-scm==9.2.0
+shapely @ file:///croot/shapely_1754380812723/work
+simplejson==3.20.1
+sip @ file:///croot/sip_1738856193618/work
+six==1.17.0
+slicerator==1.1.0
+sniffio @ file:///home/conda/feedstock_root/build_artifacts/sniffio_1733244044561/work
+snuggs @ file:///home/conda/feedstock_root/build_artifacts/snuggs_1733818638588/work
+sortedcontainers @ file:///home/conda/feedstock_root/build_artifacts/sortedcontainers_1738440353519/work
+soupsieve @ file:///home/conda/feedstock_root/build_artifacts/soupsieve_1756330469801/work
+sparse @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_sparse_1747799051/work
+SQLAlchemy==1.4.54
+stack_data @ file:///home/conda/feedstock_root/build_artifacts/stack_data_1733569443808/work
+starlette==0.50.0
+sympy==1.14.0
+tblib @ file:///home/conda/feedstock_root/build_artifacts/tblib_1743515515538/work
+terminado @ file:///home/conda/feedstock_root/build_artifacts/terminado_1710262609923/work
+text-unidecode==1.3
+threadpoolctl @ file:///home/conda/feedstock_root/build_artifacts/threadpoolctl_1741878222898/work
+tifffile==2025.5.10
+timescale==0.0.9
+timezonefinder==8.0.0
+tinycss2 @ file:///home/conda/feedstock_root/build_artifacts/tinycss2_1729802851396/work
+tomli @ file:///croot/tomli_1753774587605/work
+toolz @ file:///home/conda/feedstock_root/build_artifacts/toolz_1733736030883/work
+torch==2.9.1
+torchvision==0.24.1
+tornado @ file:///croot/tornado_1748956929273/work
+tqdm==4.67.1
+traitlets @ file:///home/conda/feedstock_root/build_artifacts/traitlets_1733367359838/work
+traittypes==0.2.1
+triton==3.5.1
+types-python-dateutil @ file:///home/conda/feedstock_root/build_artifacts/types-python-dateutil_1759899809376/work
+typing-inspection==0.4.1
+typing_extensions @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_typing_extensions_1756220668/work
+typing_utils @ file:///home/conda/feedstock_root/build_artifacts/typing_utils_1733331286120/work
+tzdata @ file:///croot/python-tzdata_1746123641790/work
+uc-micro-py==1.0.3
+unicodedata2 @ file:///croot/unicodedata2_1736541023050/work
+uri-template @ file:///home/conda/feedstock_root/build_artifacts/uri-template_1733323593477/work/dist
+urllib3 @ file:///croot/urllib3_1750775463400/work
+uvicorn==0.38.0
+wcwidth @ file:///home/conda/feedstock_root/build_artifacts/wcwidth_1733231326287/work
+webcolors @ file:///home/conda/feedstock_root/build_artifacts/webcolors_1733359735138/work
+webencodings @ file:///home/conda/feedstock_root/build_artifacts/webencodings_1733236011802/work
+websocket-client @ file:///home/conda/feedstock_root/build_artifacts/websocket-client_1759928050786/work
+Werkzeug==3.1.3
+widgetsnbextension==4.0.14
+wrapt @ file:///home/conda/feedstock_root/build_artifacts/wrapt_1651495243689/work
+xarray @ file:///home/conda/feedstock_root/build_artifacts/xarray_1749743207754/work
+xgboost==3.1.2
+xyzservices @ file:///croot/xyzservices_1675159059961/work
+yarl==1.20.1
+zarr @ file:///home/conda/feedstock_root/build_artifacts/zarr_1733237197728/work
+zict @ file:///home/conda/feedstock_root/build_artifacts/zict_1733261551178/work
+zipp @ file:///home/conda/feedstock_root/build_artifacts/zipp_1749421620841/work
@@ -0,0 +1,142 @@
+aiobotocore==2.25.0
+aiohappyeyeballs==2.6.1
+aiohttp==3.12.15
+aioitertools==0.12.0
+aiosignal==1.4.0
+alembic==1.16.5
+annotated-doc==0.0.4
+annotated-types==0.7.0
+asciitree==0.3.3
+async-timeout==3.0.1
+blinker==1.9.0
+bokeh==3.7.3
+boto3==1.40.18
+botocore==1.40.49
+cachetools==6.2.0
+Cartopy==0.25.0
+ciso8601==2.3.3
+colorama==0.4.6
+colorcet==3.1.0
+cytoolz==0.11.2
+dask-image==2024.5.3
+datacube==1.9.4
+datacube_ows==1.9.4
+datashader==0.18.2
+dea-tools==0.3.0
+deepdiff==8.6.1
+eo-tides==0.8.2
+fastapi==0.124.3
+filelock==3.19.1
+fiona==1.10.1
+Flask==3.1.2
+flask-babel==4.0.0
+flatbuffers==25.2.10
+folium==0.20.0
+frozenlist==1.7.0
+geographiclib==2.1
+geojson==3.2.0
+geomad==1.0.0
+geopy==2.4.1
+git-filter-repo==2.47.0
+h3==4.3.1
+hdstats==0.2.1
+holoviews==1.21.0
+hvplot==0.12.1
+idna==3.10
+imagecodecs==2025.3.30
+imageio==2.37.0
+ipyleaflet==0.20.0
+ipywidgets==8.1.7
+iso8601==2.1.0
+itsdangerous==2.2.0
+jsonschema-specifications==2025.4.1
+jupyter-leaflet==0.20.0
+jupyter-ui-poll==1.0.0
+jupyterlab_widgets==3.0.15
+lark==1.2.2
+lark-parser==0.12.0
+lazy_loader==0.4
+linkify-it-py==2.0.3
+lxml==5.4.0
+Markdown==3.9
+markdown-it-py==4.0.0
+matplotlib==3.10.5
+mdit-py-plugins==0.5.0
+mdurl==0.1.2
+mpmath==1.3.0
+narwhals==2.3.0
+nvidia-cublas-cu12==12.8.4.1
+nvidia-cuda-cupti-cu12==12.8.90
+nvidia-cuda-nvrtc-cu12==12.8.93
+nvidia-cuda-runtime-cu12==12.8.90
+nvidia-cudnn-cu12==9.10.2.21
+nvidia-cufft-cu12==11.3.3.83
+nvidia-cufile-cu12==1.13.1.3
+nvidia-curand-cu12==10.3.9.90
+nvidia-cusolver-cu12==11.7.3.90
+nvidia-cusparse-cu12==12.5.8.93
+nvidia-cusparselt-cu12==0.7.1
+nvidia-nccl-cu12==2.27.5
+nvidia-nvjitlink-cu12==12.8.93
+nvidia-nvshmem-cu12==3.3.20
+nvidia-nvtx-cu12==12.8.90
+odc-algo==0.2.3
+odc-geo==0.4.10
+odc-io==0.2.2
+odc-ui==0.2.1
+orderly-set==5.5.0
+OWSLib==0.34.1
+panel==1.7.5
+param==2.2.1
+PIMS==0.7
+planetary-computer==1.0.0
+prometheus_client==0.22.1
+prometheus_flask_exporter==0.23.2
+propcache==0.3.2
+pyct==0.5.0
+pydantic==2.11.7
+pydantic_core==2.33.2
+pyows==0.3.1
+PyQt6==6.7.1
+pyshp==2.3.1
+pystac-client==0.9.0
+python-dateutil==2.9.0.post0
+python-dotenv==1.1.1
+python-multipart==0.0.21
+python-slugify==8.0.4
+pyTMD==2.2.8
+pyviz_comms==3.0.6
+PyYAML==6.0.2
+rasterstats==0.20.0
+referencing==0.36.2
+regex==2025.9.1
+rfc3987==1.3.8
+ruamel.yaml.clib==0.2.12
+s3fs==2025.9.0
+s3transfer==0.13.1
+scikit-image==0.25.2
+scikit-learn==1.7.1
+seaborn==0.13.2
+setuptools-scm==9.2.0
+simplejson==3.20.1
+six==1.17.0
+slicerator==1.1.0
+SQLAlchemy==2.0.0
+starlette==0.50.0
+sympy==1.14.0
+text-unidecode==1.3
+tifffile==2025.5.10
+timescale==0.0.9
+timezonefinder==8.0.0
+torch==2.9.1
+torchvision==0.24.1
+tqdm==4.67.1
+traittypes==0.2.1
+triton==3.5.1
+typing-inspection==0.4.1
+uc-micro-py==1.0.3
+uvicorn==0.38.0
+Werkzeug==3.1.3
+widgetsnbextension==4.0.14
+xgboost==3.1.2
+yarl==1.20.1
@@ -0,0 +1,48 @@
+#!/usr/bin/env python3
+"""
+Test script to verify shapefile overlay API returns correct bbox data
+"""
+
+import requests
+import json
+
+def test_shapefile_api():
+    """Test /api/overlay/shapefiles endpoint"""
+    print("Testing /api/overlay/shapefiles endpoint...")
+    
+    try:
+        response = requests.get('http://localhost:8000/api/overlay/shapefiles')
+        
+        if response.status_code == 200:
+            data = response.json()
+            print(f"\n✅ API Response successful")
+            print(f"Total shapefiles: {data.get('count', 0)}")
+            
+            if data.get('shapefiles'):
+                print("\n📋 Shapefile details:")
+                for idx, shp in enumerate(data['shapefiles'], 1):
+                    print(f"\n{idx}. {shp.get('filename')}")
+                    print(f"   Path: {shp.get('path')}")
+                    print(f"   CRS: {shp.get('crs')}")
+                    print(f"   Features: {shp.get('feature_count')}")
+                    print(f"   Bbox: {shp.get('bbox')}")
+                    
+                    # Verify bbox format
+                    bbox = shp.get('bbox')
+                    if bbox and len(bbox) == 4:
+                        print(f"   ✅ Bbox format valid: [minLon, minLat, maxLon, maxLat]")
+                    else:
+                        print(f"   ❌ Bbox format invalid or missing!")
+            else:
+                print("\n⚠️ No shapefiles found")
+        else:
+            print(f"\n❌ API returned status code: {response.status_code}")
+            print(f"Response: {response.text}")
+            
+    except requests.exceptions.ConnectionError:
+        print("\n❌ Cannot connect to API server. Is it running on localhost:8000?")
+    except Exception as e:
+        print(f"\n❌ Error: {e}")
+
+if __name__ == "__main__":
+    test_shapefile_api()
@@ -0,0 +1,79 @@
+#!/usr/bin/env python
+"""
+Test script to verify shapefile overlay functionality
+"""
+import geopandas as gpd
+import numpy as np
+from pathlib import Path
+
+# Test shapefile path
+shapefile_path = "ChauThanh/HienTrang/ChauThanh_kiemke.shp"
+
+print("=" * 70)
+print("TESTING SHAPEFILE OVERLAY")
+print("=" * 70)
+
+# Check if file exists
+shp = Path(shapefile_path)
+print(f"\n1. Checking file existence:")
+print(f"   Path: {shp}")
+print(f"   Exists: {shp.exists()}")
+print(f"   Absolute: {shp.absolute()}")
+
+if shp.exists():
+    # Read shapefile
+    print(f"\n2. Reading shapefile...")
+    gdf = gpd.read_file(str(shp))
+    print(f"   Features: {len(gdf)}")
+    print(f"   CRS: {gdf.crs}")
+    print(f"   Bounds: {gdf.total_bounds}")
+    print(f"   Columns: {list(gdf.columns)}")
+    
+    # Check geometries
+    print(f"\n3. Checking geometries...")
+    valid_count = sum(1 for geom in gdf.geometry if geom is not None and geom.is_valid)
+    print(f"   Valid geometries: {valid_count} / {len(gdf)}")
+    
+    # Sample geometry bounds
+    if len(gdf) > 0:
+        sample_geom = gdf.geometry.iloc[0]
+        print(f"   Sample geometry type: {sample_geom.geom_type}")
+        print(f"   Sample geometry bounds: {sample_geom.bounds}")
+    
+    # Test reprojection to EPSG:4326
+    print(f"\n4. Testing reprojection to EPSG:4326...")
+    try:
+        gdf_4326 = gdf.to_crs("EPSG:4326")
+        print(f"   Success!")
+        print(f"   New bounds: {gdf_4326.total_bounds}")
+    except Exception as e:
+        print(f"   ERROR: {e}")
+    
+    # Test boundary extraction
+    print(f"\n5. Testing boundary extraction...")
+    boundaries = []
+    for geom in gdf.geometry:
+        if geom is not None and geom.is_valid:
+            boundary = geom.boundary
+            if boundary is not None:
+                boundaries.append(boundary)
+    print(f"   Extracted boundaries: {len(boundaries)}")
+    
+    # Test buffering
+    print(f"\n6. Testing buffer...")
+    buffer_size = 0.001  # degrees or meters depending on CRS
+    buffered = []
+    for boundary in boundaries[:10]:  # Test first 10
+        try:
+            buf = boundary.buffer(buffer_size)
+            buffered.append(buf)
+        except Exception as e:
+            print(f"   Buffer error: {e}")
+    print(f"   Successfully buffered: {len(buffered)} / 10")
+    
+else:
+    print("   ERROR: Shapefile not found!")
+
+print("\n" + "=" * 70)
+print("TEST COMPLETE")
+print("=" * 70)
@@ -0,0 +1,177 @@
+<!DOCTYPE html>
+<html lang="vi">
+<head>
+    <meta charset="UTF-8">
+    <title>Test Shapefile Selection</title>
+    <link rel="stylesheet" href="https://unpkg.com/leaflet@1.9.4/dist/leaflet.css" />
+    <style>
+        body { font-family: Arial, sans-serif; padding: 20px; }
+        #map { height: 400px; border: 2px solid #ccc; margin: 20px 0; }
+        .info-box { background: #f0f0f0; padding: 15px; margin: 10px 0; border-radius: 5px; }
+    </style>
+</head>
+<body>
+    <h1>🧪 Test Shapefile Auto-Select Bbox</h1>
+    
+    <div class="info-box">
+        <h3>Chọn Shapefile:</h3>
+        <select id="shapefileOverlay" onchange="onShapefileSelected(event)" style="width: 100%; padding: 10px; font-size: 14px;">
+            <option value="">-- Chọn shapefile --</option>
+        </select>
+    </div>
+    
+    <div id="map"></div>
+    
+    <div class="info-box">
+        <h3>Current Bbox:</h3>
+        <pre id="bboxInfo">Chưa chọn shapefile</pre>
+    </div>
+    
+    <div class="info-box">
+        <h3>Console Logs:</h3>
+        <pre id="console" style="max-height: 200px; overflow-y: auto; background: #000; color: #0f0; padding: 10px;"></pre>
+    </div>
+    
+    <script src="https://unpkg.com/leaflet@1.9.4/dist/leaflet.js"></script>
+    <script>
+        // Global variables
+        let map, drawnItems, selectedBbox = null;
+        
+        // Custom console.log to display in page
+        const originalLog = console.log;
+        console.log = function(...args) {
+            originalLog.apply(console, args);
+            const consoleEl = document.getElementById('console');
+            consoleEl.textContent += args.join(' ') + '\n';
+            consoleEl.scrollTop = consoleEl.scrollHeight;
+        };
+        
+        // Initialize map
+        function initMap() {
+            map = L.map('map').setView([10.0, 105.8], 10);
+            L.tileLayer('https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png', {
+                attribution: '© OpenStreetMap contributors'
+            }).addTo(map);
+            
+            drawnItems = new L.FeatureGroup();
+            map.addLayer(drawnItems);
+            
+            console.log('✅ Map initialized');
+        }
+        
+        // Load shapefiles from API
+        async function loadShapefiles() {
+            try {
+                console.log('📡 Fetching shapefiles from API...');
+                const response = await fetch('http://localhost:8000/api/overlay/shapefiles');
+                const data = await response.json();
+                
+                const select = document.getElementById('shapefileOverlay');
+                select.innerHTML = '<option value="">-- Chọn shapefile --</option>';
+                
+                if (data.shapefiles && data.shapefiles.length > 0) {
+                    data.shapefiles.forEach(shp => {
+                        const option = document.createElement('option');
+                        option.value = shp.path;
+                        
+                        let label = `${shp.filename} - ${shp.feature_count} features`;
+                        if (shp.crs) {
+                            const crsCode = shp.crs.split(':').pop();
+                            label += ` | CRS: ${crsCode}`;
+                        }
+                        if (shp.bbox && shp.bbox.length === 4) {
+                            const [minLon, minLat, maxLon, maxLat] = shp.bbox;
+                            label += ` | [${minLon.toFixed(2)}, ${minLat.toFixed(2)}, ${maxLon.toFixed(2)}, ${maxLat.toFixed(2)}]`;
+                        }
+                        
+                        option.textContent = label;
+                        option.dataset.crs = shp.crs || '';
+                        option.dataset.bbox = JSON.stringify(shp.bbox || []);
+                        option.dataset.featureCount = shp.feature_count;
+                        
+                        select.appendChild(option);
+                    });
+                    
+                    console.log(`✅ Loaded ${data.shapefiles.length} shapefiles`);
+                } else {
+                    console.log('⚠️ No shapefiles found');
+                }
+            } catch (error) {
+                console.error('❌ Error loading shapefiles:', error);
+            }
+        }
+        
+        // Handle shapefile selection
+        function onShapefileSelected(event) {
+            console.log('🔔 Shapefile selection changed');
+            
+            const selectedOption = event.target.selectedOptions[0];
+            
+            if (!selectedOption || !selectedOption.value) {
+                console.log('ℹ️ No shapefile selected');
+                document.getElementById('bboxInfo').textContent = 'Chưa chọn shapefile';
+                return;
+            }
+            
+            const bboxData = selectedOption.dataset.bbox;
+            console.log('📦 Bbox data from option:', bboxData);
+            
+            if (!bboxData || bboxData === '[]') {
+                console.log('⚠️ No bbox data in selected option');
+                return;
+            }
+            
+            try {
+                const bbox = JSON.parse(bboxData);
+                console.log('📊 Parsed bbox:', bbox);
+                
+                if (bbox.length !== 4) {
+                    console.log('❌ Invalid bbox length:', bbox.length);
+                    return;
+                }
+                
+                const [minLon, minLat, maxLon, maxLat] = bbox;
+                
+                // Validate bbox
+                if (minLon < -180 || maxLon > 180 || minLat < -90 || maxLat > 90) {
+                    console.log('❌ Bbox out of valid range');
+                    return;
+                }
+                
+                console.log('✅ Valid bbox:', {minLon, minLat, maxLon, maxLat});
+                
+                // Update map
+                const bounds = [[minLat, minLon], [maxLat, maxLon]];
+                const rectangle = L.rectangle(bounds, {
+                    color: '#667eea',
+                    weight: 3,
+                    fillOpacity: 0.2
+                });
+                
+                drawnItems.clearLayers();
+                drawnItems.addLayer(rectangle);
+                map.fitBounds(bounds, { padding: [50, 50] });
+                
+                selectedBbox = {min_lon: minLon, min_lat: minLat, max_lon: maxLon, max_lat: maxLat};
+                
+                console.log('🗺️ Map updated with new bbox');
+                
+                // Update bbox info display
+                document.getElementById('bboxInfo').textContent = JSON.stringify(selectedBbox, null, 2);
+                
+                alert(`✅ Bbox updated!\n\nmin_lon: ${minLon.toFixed(4)}\nmin_lat: ${minLat.toFixed(4)}\nmax_lon: ${maxLon.toFixed(4)}\nmax_lat: ${maxLat.toFixed(4)}`);
+                
+            } catch (e) {
+                console.error('❌ Error:', e);
+            }
+        }
+        
+        // Initialize on load
+        window.onload = function() {
+            console.log('🚀 Page loaded, initializing...');
+            initMap();
+            loadShapefiles();
+        };
+    </script>
+</body>
+</html>
Author	SHA1	Message	Date
basketballcantho	a4ad11742a	bổ sung mô tả dự án	2026-03-27 09:18:38 +07:00
basketballcantho	f2c11079e4	hoàn thành chức năng phân lô trên ảnh predict	2026-02-18 16:22:18 +07:00
basketballcantho	b5b600023e	update requirement.txt	2026-02-17 22:13:35 +07:00