admin swin-unet train file

.gitignore

@@ -76,11 +76,9 @@ model_train/*.feather
 model_train/*.db
 model_train/*.sqlite
 model_train/*.log
-cloud_removal_model/
 
 # VSCode settings
 .vscode/
 
 # Jupyter checkpoints
 .ipynb_checkpoints/
-reports/

.stfolder/syncthing-folder-a35f20.txt

@@ -0,0 +1,5 @@
+# This directory is a Syncthing folder marker.
+# Do not delete.
+
+folderID: rs-data
+created: 2026-04-03T11:35:19+07:00

.temp/01.train_ODC_XGBoost.py

@@ -1,171 +0,0 @@
-#!/usr/bin/env python
-# coding: utf-8
-
-# In[1]:
-
-
-get_ipython().run_cell_magic('time', '', '%matplotlib inline\n\nimport importlib\nimport new_import_ODC  \n\nimportlib.reload(new_import_ODC)\n\nfrom new_import_ODC import *\n')
-
-
-# In[2]:
-
-
-get_ipython().run_cell_magic('time', '', '# Cấu hình Daskgateway\ncluster, client = notebook_utils.initialize_dask(use_gateway=True, workers=(1, 10))\n# Khai báo 1 Datacube là dc\ndc = None\n\n# Cấu hình truy cập dịch vụ S3\nconfigure_s3_access(aws_unsigned=False, requester_pays=True, client=client)\n\nclient\n')
-
-
-# In[3]:
-
-
-## cấu hình thời gian lấy ảnh và tọa độ
-date_range = ("2022-09-01", "2022-10-01")
-longtitude_range = (105.86, 105.94)
-latitude_range = (9.65, 9.69)
-
-coordinates = (longtitude_range, latitude_range)
-
-
-# In[4]:
-
-
-## truy vấn ảnh vệ tinh sen2
-data = load_data(None, date_range, longtitude_range, latitude_range)
-notebook_utils.heading(notebook_utils.xarray_object_size(data))
-display(data)
-
-
-# In[5]:
-
-
-get_ipython().run_cell_magic('time', '', '# Tiến hành loại bỏ các vị trí bị mây ảnh hưởng\nresult = mask_clean(data)\n# progress(result)\n')
-
-
-# In[6]:
-
-
-# Tiến hành tính toán NDVI
-ds1 = calculate_indices(result, index="NDVI", satellite_mission="s2")
-ndvi = ds1["NDVI"]
-display(ndvi)
-
-
-# In[7]:
-
-
-## Hiển thị ảnh NDVI chưa điền các giá trị mây (chưa fill nan)
-plt.imshow(ndvi.isel(time=0))
-
-
-# In[8]:
-
-
-# Thiết lập giá trị trung bình mùa vụ để xử lý các điểm ảnh bị mây dựa vào sự thay đổi theo mùa
-time_split = [
-    slice("2022-09-01", "2023-01-01"),
-    slice("2023-01-01", "2023-05-01"),
-    slice("2023-05-01", "2023-07-01"),
-    slice("2023-07-01", "2022-10-01"),
-]
-
-# Điền mây ở các vị trí mang giá trị nan (fill nan)
-fill_nan_ndvi = fill_nan(ndvi, time_split)
-
-# In kết quả ảnh NDVI đã điền mây (đã fill nan)
-plt.imshow(fill_nan_ndvi.isel(time=0))
-
-
-# In[9]:
-
-
-get_ipython().run_cell_magic('time', '', '## tính ndvi theo tháng\naverage_ndvi = fill_nan_ndvi.resample(time="1M").mean().persist()\n# progress(average_ndvi)\n\n# compute average_ndvi\naverage_ndvi = average_ndvi.compute()\n')
-
-
-# In[10]:
-
-
-#Load dữ liệu ảnh Sentinel 1
-dsvh, dsvv = load_data_sen1(None, date_range, coordinates)
-average_vv = calculate_average(dsvv, time_pattern='1M')
-average_vh = calculate_average(dsvh, time_pattern='1M')
-
-
-# In[11]:
-
-
-## cấu hình bộ dữ liệu điểm huấn luyện mô hình (train file)
-train_path = "train/ST_training_data_updated_1130points_new.shp"  # đường dẫn shp file train
-
-## load dữ liệu điểm huấn luyện mô hình (train file)
-train = load_train_data(train_path)
-train.head()
-
-# cấu hình nhãn dữ liệu 
-label_mapping = {
-    "Lua tom": "0",
-    "Lua": "1",
-    "CHN": "2",
-    "CLN": "3",
-    "TS": "4",
-    "Song": "5",
-    "Dat xay dung": "6",
-    "Rung": "7",
-}
-
-# xây dựng tập dữ liệu (dataset) chứa dữ liệu VH, VV, NDVI
-datasets = get_data_sen1_and_sen2(train, average_ndvi, average_vh, average_vv)
-
-# chia tập dữ liệu thành các phần theo tỉ lệ 80(80-20)-20 tương ứng với tập train, validate, test
-X_train, X_val, X_test, y_train, y_val, y_test = split_train_data(
-    train, label_mapping, datasets
-)
-
-
-# In[ ]:
-
-
-get_ipython().run_cell_magic('time', '', '# Import XGBoost\nimport xgboost as xgb\nfrom sklearn.metrics import accuracy_score\nimport numpy as np\n\n# Convert to numpy arrays\nX_train_np = np.asarray(X_train, dtype=np.float32)\nX_val_np = np.asarray(X_val, dtype=np.float32)\ny_train_np = np.asarray(y_train, dtype=np.int32)\ny_val_np = np.asarray(y_val, dtype=np.int32)\n\nprint("🚀 Training XGBoost model...")\nprint(f"   Train samples: {len(X_train_np)}")\nprint(f"   Val samples: {len(X_val_np)}")\nprint(f"   Features: {X_train_np.shape[1]}")\nprint(f"   Classes: 8\\n")\n\n# XGBoost parameters\nparams = {\n    \'objective\': \'multi:softmax\',  # Multi-class classification\n    \'num_class\': 8,                 # 8 land use classes\n    \'max_depth\': 6,                 # Maximum tree depth\n    \'learning_rate\': 0.1,           # Learning rate\n    \'n_estimators\': 200,            # Number of trees\n    \'subsample\': 0.8,               # Subsample ratio\n    \'colsample_bytree\': 0.8,        # Feature sampling ratio\n    \'random_state\': 42,\n    \'n_jobs\': -1,                   # Use all CPU cores\n    \'eval_metric\': \'mlogloss\'       # Multi-class log loss\n}\n\n# Train XGBoost model\nmodel = xgb.XGBClassifier(**params)\n\nmodel.fit(\n    X_train_np, y_train_np,\n    eval_set=[(X_train_np, y_train_np), (X_val_np, y_val_np)],\n    verbose=True\n)\n\n# Validation accuracy\ny_val_pred = model.predict(X_val_np)\nval_accuracy = accuracy_score(y_val_np, y_val_pred)\nprint(f"\\n✅ Training completed!")\nprint(f"   Validation Accuracy: {val_accuracy:.4f} ({val_accuracy*100:.2f}%)")\n')
-
-
-# In[ ]:
-
-
-get_ipython().run_cell_magic('time', '', '# Evaluate on test set\nX_test_np = np.asarray(X_test, dtype=np.float32)\ny_test_np = np.asarray(y_test, dtype=np.int32)\n\nprint("📊 Evaluating XGBoost model on test set...\\n")\n\n# Predictions\ny_pred_test = model.predict(X_test_np)\n\n# Metrics\nfrom sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix\n\ntest_accuracy = accuracy_score(y_test_np, y_pred_test)\nprecision = precision_score(y_test_np, y_pred_test, average=\'weighted\', zero_division=0)\nrecall = recall_score(y_test_np, y_pred_test, average=\'weighted\', zero_division=0)\nf1 = f1_score(y_test_np, y_pred_test, average=\'weighted\', zero_division=0)\n\nprint(f"📈 Test Results:")\nprint(f"   Accuracy:  {test_accuracy:.4f} ({test_accuracy*100:.2f}%)")\nprint(f"   Precision: {precision:.4f}")\nprint(f"   Recall:    {recall:.4f}")\nprint(f"   F1-Score:  {f1:.4f}\\n")\n\n# Confusion Matrix\nfrom sklearn.metrics import ConfusionMatrixDisplay\nimport matplotlib.pyplot as plt\n\n# Create figure first\nfig, ax = plt.subplots(figsize=(10, 8))\n\nclass_names = list(label_mapping.keys())\ncm = confusion_matrix(y_test_np, y_pred_test)\ndisp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=class_names)\ndisp.plot(cmap=\'Blues\', ax=ax)\nplt.xticks(rotation=45, ha=\'right\')\nplt.title(\'XGBoost Confusion Matrix\')\nplt.tight_layout()\nplt.show()\n')
-
-
-# In[ ]:
-
-
-# Lưu mô hình huấn luyện
-import json
-import joblib
-
-# Save XGBoost model
-model_path = "model_xgboost.joblib"
-joblib.dump(model, model_path)
-print(f"✅ Model saved to {model_path}")
-
-# Save model info
-info = {
-    "model_type": "XGBoost",
-    "num_classes": 8,
-    "classes": list(label_mapping.keys()),
-    "num_features": X_train_np.shape[1],
-    "params": params,
-    "accuracy": float(test_accuracy),
-    "precision": float(precision),
-    "recall": float(recall),
-    "f1_score": float(f1),
-}
-
-with open("model_xgboost_info.json", "w") as f:
-    json.dump(info, f, indent=2)
-
-print(f"✅ Model info saved to model_xgboost_info.json")
-
-
-# In[15]:
-
-
-# đóng client, cluster
-# client.close()
-# cluster.close()
-

.temp/01.train_ODC_local_with_Mic_supplyer.py

@@ -1,56 +0,0 @@
-#!/usr/bin/env python
-# coding: utf-8
-
-# In[6]:
-
-
-get_ipython().run_cell_magic('time', '', '%matplotlib inline\n\n# Import Microsoft Planetary Computer libraries\nimport planetary_computer\nfrom pystac_client import Client\nfrom odc.stac import load as stac_load\n\n# Standard imports\nimport xarray as xr\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score, classification_report, confusion_matrix, ConfusionMatrixDisplay\nimport geopandas as gpd\n\n# XGBoost for GPU training\nimport xgboost as xgb\n\nfrom xgboost import XGBClassifier\n\nprint(f"   XGBoost version: {xgb.__version__}")\n\nprint("✅ All modules loaded successfully")\n')
-
-
-# In[7]:
-
-
-get_ipython().run_cell_magic('time', '', '# Kết nối tới Microsoft Planetary Computer STAC\nfrom pystac_client import Client\n\n# KHÔNG dùng modifier ở catalog level để tránh items bị convert thành dict\ncatalog = Client.open(\n    "https://planetarycomputer.microsoft.com/api/stac/v1"\n)\nprint("✅ Connected to Microsoft Planetary Computer")\n\nprint("\\n" + "="*70)\n')
-
-
-# In[8]:
-
-
-get_ipython().run_cell_magic('time', '', '# 🌍 Định nghĩa khu vực và thời gian\nprint("="*70)\nprint("CONFIGURATION")\nprint("="*70)\n\n# Khu vực quan tâm (Vietnam - Mekong Delta) - GIẢM DIỆN TÍCH ~40%\nbbox = [105.6, 9.3, 106.2, 9.8]  # [min_lon, min_lat, max_lon, max_lat]\n\n# GIẢM THỜI GIAN xuống 3 tháng để giảm kích thước dữ liệu cho PC\ntime_range = "2023-03-01/2023-05-31"  # 3 tháng (mùa khô)\n\nprint(f"\\n📍 Area of Interest:")\nprint(f"   Longitude: {bbox[0]} to {bbox[2]}")\nprint(f"   Latitude: {bbox[1]} to {bbox[3]}")\nprint(f"\\n📅 Time Range: {time_range}")\nprint(f"   ⚠️  Optimized for personal computer (3 months, reduced area)")\nprint(f"\\n🗺️  CRS: EPSG:32648")\nprint(f"   Resolution: 20m (reduced from 10m for smaller data size)")\n\nprint("="*70)\n')
-
-
-# In[9]:
-
-
-get_ipython().run_cell_magic('time', '', '# 📡 LOAD SENTINEL-2 FROM MICROSOFT PLANETARY COMPUTER\nprint("="*70)\nprint("LOADING SENTINEL-2 L2A")\nprint("="*70)\n\nprint("\\n🔍 Searching for Sentinel-2 scenes...")\nquery_s2 = catalog.search(\n    collections=["sentinel-2-l2a"],\n    bbox=bbox,\n    datetime=time_range,\n    query={"eo:cloud_cover": {"lt": 30}}  # Cloud cover < 30% (giảm từ 50%)\n)\n\nitems_s2 = list(query_s2.item_collection())\nprint(f"✅ Found {len(items_s2)} Sentinel-2 scenes")\n\n# GIỚI HẠN SỐ LƯỢNG SCENES cho PC cá nhân\nmax_scenes = 12  # Giảm xuống 12 scenes để tối ưu cho PC\nif len(items_s2) > max_scenes:\n    print(f"⚠️  Limiting to {max_scenes} scenes for personal computer")\n    # Chọn scenes đều đặn trong khoảng thời gian\n    step = len(items_s2) // max_scenes\n    items_s2 = items_s2[::step][:max_scenes]\n    print(f"   Selected {len(items_s2)} scenes evenly distributed")\n\nif len(items_s2) > 0:\n    # Show first few scenes\n    print(f"\\n📋 Sample scenes:")\n    for i, item in enumerate(items_s2[:5]):\n        date = item.datetime.strftime("%Y-%m-%d")\n        cloud = item.properties.get("eo:cloud_cover", "N/A")\n        print(f"   [{i+1}] {date} - Cloud: {cloud}%")\n    \n    # Re-sign items to ensure fresh URLs (keep as pystac objects)\n    print(f"\\n🔑 Signing STAC items...")\n    items_s2 = [planetary_computer.sign(item) for item in items_s2]\n    \n    # Load Sentinel-2 data (without Dask chunks)\n    print(f"\\n⏳ Loading Sentinel-2 data...")\n    ds_s2 = stac_load(\n        items_s2,\n        bands=["B04", "B08", "SCL"],  # Red (B04), NIR (B08), Scene Classification (SCL)\n        crs="EPSG:32648",\n        resolution=20,  # 20m resolution (4x smaller data than 10m)\n        bbox=bbox,\n        patch_url=planetary_computer.sign,  # Re-sign URLs during loading\n        fail_on_error=False,  # Skip problematic tiles instead of crashing\n    )\n    \n    # Rename bands to simpler names\n    ds_s2 = ds_s2.rename({"B04": "red", "B08": "nir", "SCL": "scl"})\n    \n    print(f"\\n✅ Sentinel-2 loaded!")\n    print(f"   Shape: {dict(ds_s2.dims)}")\n    print(f"   Variables: {list(ds_s2.data_vars)}")\n    display(ds_s2)\nelse:\n    print(f"❌ No Sentinel-2 scenes found")\n\n    ds_s2 = Noneprint("="*70)\n')
-
-
-# In[10]:
-
-
-get_ipython().run_cell_magic('time', '', '# 📡 LOAD SENTINEL-1 FROM MICROSOFT PLANETARY COMPUTER\nprint("="*70)\nprint("LOADING SENTINEL-1 RTC")\nprint("="*70)\n\nprint("\\n🔍 Searching for Sentinel-1 scenes...")\nquery_s1 = catalog.search(\n    collections=["sentinel-1-rtc"],\n    bbox=bbox,\n    datetime=time_range,\n)\n\nitems_s1 = list(query_s1.item_collection())\nprint(f"✅ Found {len(items_s1)} Sentinel-1 scenes")\n\n# GIỚI HẠN SỐ LƯỢNG SCENES cho PC cá nhân\nmax_scenes = 12  # Giảm xuống 12 scenes để tối ưu cho PC\nif len(items_s1) > max_scenes:\n    print(f"⚠️  Limiting to {max_scenes} scenes for personal computer")\n    # Chọn scenes đều đặn trong khoảng thời gian\n    step = len(items_s1) // max_scenes\n    items_s1 = items_s1[::step][:max_scenes]\n    print(f"   Selected {len(items_s1)} scenes evenly distributed")\n\nif len(items_s1) > 0:\n    # Show first few scenes\n    print(f"\\n📋 Sample scenes:")\n    for i, item in enumerate(items_s1[:5]):\n        date = item.datetime.strftime("%Y-%m-%d")\n        orbit = item.properties.get("sat:orbit_state", "N/A")\n        print(f"   [{i+1}] {date} - Orbit: {orbit}")\n    \n    # Re-sign items to ensure fresh URLs (keep as pystac objects)\n    print(f"\\n🔑 Signing STAC items...")\n    items_s1 = [planetary_computer.sign(item) for item in items_s1]\n    \n    # Load Sentinel-1 data (without Dask chunks)\n    print(f"\\n⏳ Loading Sentinel-1 data...")\n    ds_s1 = stac_load(\n        items_s1,\n        bands=["vv", "vh"],  # VV and VH polarizations\n        crs="EPSG:32648",\n        resolution=20,  # 20m resolution (4x smaller data than 10m)\n        bbox=bbox,\n        patch_url=planetary_computer.sign,  # Re-sign URLs during loading\n        fail_on_error=False,  # Skip problematic tiles instead of crashing\n    )\n    \n    # Convert to dB (Microsoft S1 is in linear power)\n    print(f"\\n🔄 Converting to dB...")\n    ds_s1[\'vv_db\'] = 10 * np.log10(ds_s1[\'vv\'].where(ds_s1[\'vv\'] > 0))\n    ds_s1[\'vh_db\'] = 10 * np.log10(ds_s1[\'vh\'].where(ds_s1[\'vh\'] > 0))\n    \n    print(f"\\n✅ Sentinel-1 loaded!")\n    print(f"   Shape: {dict(ds_s1.dims)}")\n    print(f"   Variables: {list(ds_s1.data_vars)}")\n    display(ds_s1)\nelse:\n    print(f"❌ No Sentinel-1 scenes found")\n\n    ds_s1 = Noneprint("="*70)\n')
-
-
-# In[11]:
-
-
-get_ipython().run_cell_magic('time', '', '# 🌿 CALCULATE NDVI AND PROCESS DATA\nprint("="*70)\nprint("DATA PROCESSING")\nprint("="*70)\n\nif ds_s2 is not None:\n    print("\\n[1] Calculating NDVI...")\n    # NDVI = (NIR - Red) / (NIR + Red)\n    ndvi = (ds_s2[\'nir\'] - ds_s2[\'red\']) / (ds_s2[\'nir\'] + ds_s2[\'red\'] + 1e-8)\n    \n    print(f"✅ NDVI calculated")\n    print(f"   Shape: {ndvi.shape}")\n    print(f"   Time steps: {len(ndvi.time)}")\n    \n    # Cloud masking using SCL band\n    print(f"\\n[2] Applying cloud mask...")\n    # SCL values: 1=defective, 3=cloud shadow, 8=cloud medium, 9=cloud high, 10=cirrus\n    cloud_mask = ds_s2[\'scl\'].isin([1, 3, 8, 9, 10])\n    ndvi_masked = ndvi.where(~cloud_mask)\n    \n    print(f"✅ Cloud mask applied")\n    \n    # Temporal aggregation (mean over time)\n    print(f"\\n[3] Computing mean NDVI across time...")\n    ndvi_mean = ndvi_masked.mean(dim=\'time\')\n    \n    # Data already in memory, no need to compute() again\n    print(f"✅ Mean NDVI computed")\n    print(f"   Shape: {ndvi_mean.shape}")\n    \nelse:\n    print("❌ No Sentinel-2 data to process")\n    ndvi_mean = None\n\nprint("="*70)\n')
-
-
-# In[ ]:
-
-
-get_ipython().run_cell_magic('time', '', '# 🎯 EXTRACT TRAINING DATA FEATURES\nprint("="*70)\nprint("FEATURE EXTRACTION")\nprint("="*70)\n\n# Check if required data is available\nif \'ndvi_mean\' not in globals() or \'ds_s1\' not in globals():\n    print("❌ Error: Please run Cell 6 (DATA PROCESSING) first!")\n    print("   Required variables: ndvi_mean, ds_s1")\n    raise RuntimeError("Missing required data. Run cells in order: Cell 4 → Cell 5 → Cell 6 → Cell 7")\n\n# Load training shapefile\nimport geopandas as gpd\n\ntrain_path = \'train/ST_training data_updated_1130points_new.shp\'\nprint(f"\\n[1] Loading training data from: {train_path}")\ntrain_gdf = gpd.read_file(train_path)\n\n# Ensure CRS matches\nif train_gdf.crs != \'EPSG:32648\':\n    print(f"   Reprojecting from {train_gdf.crs} to EPSG:32648...")\n    train_gdf = train_gdf.to_crs(\'EPSG:32648\')\n\nprint(f"✅ Loaded {len(train_gdf)} training points")\nprint(f"   Available columns: {list(train_gdf.columns)}")\n\n# Auto-detect label column (look for common names)\nlabel_column = None\nfor col in [\'HT_code\', \'Ma_LU\', \'LU2022\', \'class\', \'Class\', \'CLASS\', \'label\', \'Label\', \'LABEL\', \'LU_CODE\', \'LU_code\']:\n    if col in train_gdf.columns:\n        label_column = col\n        break\n\nif label_column is None:\n    print(f"❌ Cannot find label column. Available columns: {list(train_gdf.columns)}")\n    print(f"   Please check your shapefile and update the code.")\nelse:\n    print(f"   Using label column: \'{label_column}\'")\n    print(f"   Classes: {sorted(train_gdf[label_column].unique())}")\n    \n    # Extract features at each training point\n    print(f"\\n[2] Extracting features at training points...")\n    \n    features = []\n    labels = []\n    skipped = 0\n    \n    for idx, row in train_gdf.iterrows():\n        point = row.geometrychro\n        x_coord = point.x\n        y_coord = point.y\n        label = row[label_column]\n        \n        # Extract NDVI at this location\n        if ndvi_mean is not None and ds_s1 is not None:\n            try:\n                ndvi_val = ndvi_mean.sel(x=x_coord, y=y_coord, method=\'nearest\').values\n                \n                # Extract Sentinel-1 VH/VV at this location (mean across time)\n                # Data already in memory, no need to compute()\n                vh_val = ds_s1[\'vh_db\'].sel(x=x_coord, y=y_coord, method=\'nearest\').mean(dim=\'time\').values\n                vv_val = ds_s1[\'vv_db\'].sel(x=x_coord, y=y_coord, method=\'nearest\').mean(dim=\'time\').values\n                \n                # Create feature vector: [NDVI, VH_dB, VV_dB]\n                feature_vec = [ndvi_val, vh_val, vv_val]\n                \n                # Only add if all features are valid (not NaN)\n                if not np.isnan(feature_vec).any():\n                    features.append(feature_vec)\n                    labels.append(label)\n                else:\n                    skipped += 1\n            except Exception as e:\n                # Skip points outside the data extent\n                skipped += 1\n                continue\n    \n    features = np.array(features)\n    labels = np.array(labels)\n    \n    print(f"✅ Extracted features for {len(features)} valid points")\n    print(f"   Skipped {skipped} points (outside extent or NaN values)")\n    print(f"   Feature shape: {features.shape}")\n    print(f"   Feature names: [\'NDVI_mean\', \'VH_dB_mean\', \'VV_dB_mean\']")\n    print(f"\\n   Class distribution:")\n    unique, counts = np.unique(labels, return_counts=True)\n    for cls, cnt in zip(unique, counts):\n        print(f"      Class {cls}: {cnt} samples ({cnt/len(labels)*100:.1f}%)")\n\nprint("="*70)\n')
-
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-# In[21]:
-
-
-get_ipython().run_cell_magic('time', '', '# 🤖 TRAIN XGBOOST MODEL ON GPU (RTX 4060)\nprint("="*70)\nprint("MODEL TRAINING - GPU ACCELERATED")\nprint("="*70)\n\nfrom xgboost import XGBClassifier\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.metrics import classification_report, confusion_matrix, ConfusionMatrixDisplay\nimport matplotlib.pyplot as plt\n\n# Encode labels to ensure they are 0, 1, 2, ... n-1\nprint("\\n[1] Encoding labels...")\nlabel_encoder = LabelEncoder()\nlabels_encoded = label_encoder.fit_transform(labels)\nprint(f"✅ Original classes: {label_encoder.classes_}")\nprint(f"   Encoded as: {np.unique(labels_encoded)}")\n\n# Split data\nprint("\\n[2] Splitting data (80% train, 20% test)...")\nX_train, X_test, y_train, y_test = train_test_split(\n    features, labels_encoded, test_size=0.2, random_state=42, stratify=labels_encoded\n)\nprint(f"✅ Training samples: {len(X_train)}")\nprint(f"   Testing samples: {len(X_test)}")\n\n# Train XGBoost on GPU\nprint("\\n[3] Training XGBoost classifier on RTX 4060 GPU...")\nprint("   GPU Settings: device=\'cuda:0\'")\n\nxgb_model = XGBClassifier(\n    n_estimators=100,\n    max_depth=20,\n    learning_rate=0.1,\n    device=\'cuda:0\',           # Use GPU (updated from deprecated gpu_id)\n    tree_method=\'hist\',        # Use hist with device for GPU training\n    random_state=42,\n    eval_metric=\'mlogloss\',    # Multi-class log loss\n    verbosity=1                # Show GPU training progress\n)\n\nxgb_model.fit(X_train, y_train)\nprint(f"✅ Model trained on GPU")\n\n# Evaluate\nprint("\\n[4] Evaluating model...")\ntrain_score = xgb_model.score(X_train, y_train)\ntest_score = xgb_model.score(X_test, y_test)\nprint(f"✅ Training accuracy: {train_score:.4f}")\nprint(f"   Testing accuracy: {test_score:.4f}")\n\n# Classification report\nprint("\\n[5] Classification Report:")\ny_pred = xgb_model.predict(X_test)\nprint(classification_report(y_test, y_pred, target_names=[str(c) for c in label_encoder.classes_]))\n\n# Confusion matrix\nprint("\\n[6] Confusion Matrix:")\nfig, ax = plt.subplots(figsize=(10, 8))\ncm = confusion_matrix(y_test, y_pred)\ndisp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=label_encoder.classes_)\ndisp.plot(ax=ax, cmap=\'Blues\', values_format=\'d\')\nplt.title(\'Confusion Matrix - XGBoost GPU Model (RTX 4060)\')\nplt.tight_layout()\nplt.show()\n\nprint("="*70)\n')
-
-
-# In[23]:
-
-
-get_ipython().run_cell_magic('time', '', '# 💾 SAVE MODEL AND CLEANUP\nprint("="*70)\nprint("SAVING MODEL & CLEANUP")\nprint("="*70)\n\nimport joblib\nfrom datetime import datetime\n\n# Save model and label encoder\nmodel_filename = f"model_train/model_xgboost_gpu_{datetime.now().strftime(\'%Y%m%d_%H%M%S\')}.joblib"\nprint(f"\\n[1] Saving model to: {model_filename}")\njoblib.dump({\'model\': xgb_model, \'label_encoder\': label_encoder}, model_filename)\nprint(f"✅ Model and label encoder saved")\n\n# Save model info\ninfo = {\n    "timestamp": datetime.now().isoformat(),\n    "data_source": "Microsoft Planetary Computer STAC",\n    "collections": ["sentinel-2-l2a", "sentinel-1-rtc"],\n    "features": ["NDVI_mean", "VH_dB_mean", "VV_dB_mean"],\n    "training_samples": len(X_train),\n    "testing_samples": len(X_test),\n    "train_accuracy": float(train_score),\n    "test_accuracy": float(test_score),\n    "model_type": "XGBClassifier",\n    "device": "cuda:0",\n    "gpu_device": "RTX 4060",\n    "tree_method": "hist",\n    "n_estimators": 100,\n    "max_depth": 20,\n    "learning_rate": 0.1\n}\n\nimport json\ninfo_filename = model_filename.replace(\'.joblib\', \'_info.json\')\nwith open(info_filename, \'w\') as f:\n    json.dump(info, f, indent=2)\nprint(f"✅ Model info saved to: {info_filename}")\n\n# No cleanup needed (Dask removed)\nprint("\\n[2] Cleanup complete")\n\nprint("="*70)\n\nprint("\\n" + "="*70)\n\nprint("🎉 TRAINING COMPLETE!")\n')
-

.temp/02.predict_ODC.py

@@ -1,191 +0,0 @@
-#!/usr/bin/env python
-# coding: utf-8
-
-# In[1]:
-
-
-get_ipython().run_cell_magic('time', '', '%matplotlib inline\n\nimport importlib\nimport new_import_ODC  \n\nimportlib.reload(new_import_ODC)\n\nfrom new_import_ODC import *\n')
-
-
-# In[2]:
-
-
-get_ipython().run_cell_magic('time', '', '# Dask gateway\ncluster, client = notebook_utils.initialize_dask(use_gateway=True, workers=(1,4))\ndc = datacube.Datacube()\n\n# Configure s3 access\nconfigure_s3_access(aws_unsigned=False, requester_pays=True, client=client)\n\nclient\n')
-
-
-# In[3]:
-
-
-## cấu hình thời gian lấy ảnh và tọa độ
-date_range = ('2022-09-01', '2023-10-01')
-longtitude_range = (105.86575, 105.94120)
-latitude_range = (9.65070, 9.69850)
-
-
-# In[4]:
-
-
-## truy vấn ảnh vệ tinh sen2
-data = load_data(dc, date_range, longtitude_range, latitude_range)
-notebook_utils.heading(notebook_utils.xarray_object_size(data))
-display(data)
-
-
-# In[5]:
-
-
-get_ipython().run_cell_magic('time', '', '# Tiến hành loại bỏ các vị trí bị mây ảnh hưởng\nresult = mask_clean(data)\nprogress(result)\n')
-
-
-# In[6]:
-
-
-# Tiến hành tính toán NDVI
-ds1 = calculate_indices(result, index='NDVI', satellite_mission='s2')
-ndvi = ds1["NDVI"]
-display(ndvi)
-
-
-# In[7]:
-
-
-## ảnh NDVI chưa điền mây (fill nan)
-plt.imshow(ndvi.isel(time=50))
-
-
-# In[8]:
-
-
-# đặt thời gian các mùa
-time_split = [slice('2022-09-01', '2023-01-01'), 
-               slice('2023-01-01', '2023-05-01'),
-               slice('2023-05-01', '2023-07-01'),
-               slice('2023-07-01', '2023-10-01')]
-
-# Điền mây ở các vị trí mang giá trị nan (fill nan)
-fill_nan_ndvi = fill_nan(ndvi, time_split)
-
-# In kết quả ảnh ndvi đã điền mây (đã fill nan)
-plt.imshow(fill_nan_ndvi.isel(time=50))
-
-
-# In[9]:
-
-
-get_ipython().run_cell_magic('time', '', "## tính ndvi theo tháng\naverage_ndvi = fill_nan_ndvi.resample(time='1M').mean().persist()\nprogress(average_ndvi)\n")
-
-
-# In[10]:
-
-
-# compute average_ndvi
-average_ndvi = average_ndvi.compute()
-
-
-# In[11]:
-
-
-# load dữ liệu sen1
-coordinates = (longtitude_range, latitude_range)
-dsvh, dsvv = load_data_sen1(dc, date_range, coordinates)
-average_vv = calculate_average(dsvv, time_pattern='1M')
-average_vh = calculate_average(dsvh, time_pattern='1M')
-
-
-# In[12]:
-
-
-# load model RF
-loaded_model = joblib.load(os.path.join("model_train", "model_odc.joblib"))
-
-# dự đoán
-data_array = predict(loaded_model, data.rio.crs, average_ndvi, average_vh, average_vv)
-
-
-# In[13]:
-
-
-# cấu hình màu cho các loại đất
-colors = [
-    "#abcee9",
-    "#ffef44",
-    "#c4ff9e",
-    "#ffd6a8",
-    "#93ddda",
-    "#1aeef7",
-    "#ffa7f2",
-    "#33ee33"
-]
-labels = [
-    "Lúa tôm",
-    "Lúa",
-    "CHN",
-    "CLN",
-    "TS",
-    "Sông",
-    "Đất xây dựng",
-    "Rừng"
-]
-# hiển thị phân loại sử dụng đất
-cmap = ListedColormap(colors)
-img = data_array.plot(cmap=cmap, add_colorbar=False)
-cbar = plt.colorbar(img)
-cbar.ax.set_yticklabels(labels)
-plt.title("Phân loại sử dụng đất")
-plt.axis('off')
-plt.show()
-
-
-# In[14]:
-
-
-## cấu hình shapefile ranh giới thuận hòa và vh vv file
-thuanhoa_path = "ThuanHoa/region/ST_ThuanHoa_Boundaryofficially.shp"
-
-# cắt theo ranh giới xã thuận hòa
-region_result = cut_according_shp(thuanhoa_path, average_ndvi, data_array)
-
-
-# In[15]:
-
-
-# hiển thị kết quả phân loại sử dụng đất
-colorval = list(range(len(colors)))
-options = {
-    'title': 'Phân loại sử dụng đất',
-    'cmap': colors,
-    'clim': (0, 8),
-    'aspect': 'equal',
-    'colorbar_opts': {
-        'major_label_overrides': dict(zip(colorval, labels)),
-        'major_label_text_align': 'left',
-        'ticker': FixedTicker(ticks=colorval),
-    },
-}
-                                      
-region_result.hvplot(
-    rasterize = True,                  # Use Datashader, particularly useful for dask arrays
-    aggregator = reductions.mode(),    # Datashader selects mode value, requires 'hv.Image'
-).options(opts.Image(**options))
-
-
-# In[16]:
-
-
-# Lưu lại kết quả
-region_result.rio.to_raster("KetQuaPhanLoaiDatODC.tif")
-
-
-# In[17]:
-
-
-# đóng client, cluster
-client.close()
-cluster.close()
-
-
-# In[ ]:
-
-
-
-

.temp/03.compare_ODC.py

@@ -1,117 +0,0 @@
-#!/usr/bin/env python
-# coding: utf-8
-
-# In[1]:
-
-
-# Khai báo các thư viện cần thiết
-from new_import_ODC import *
-
-# Khai báo đường dẫn đến kết quả phân loại và dữ liệu của địa phương
-KD_path = "ThuanHoa/KhoanhDat/ThuanHoa_TKDD2022.shp"
-KetQuaPhanLoaiDat = "KetQuaPhanLoaiDatODC.tif"
-
-
-# In[2]:
-
-
-# khai báo các loại đất từ dữ liệu kiểm kê ứng với các hiện trạng được phân loại từ viễn thám
-CODE_MAP = {
-    "BHK": 2,
-    "CLN": 3,
-    "DGD": 6,
-    "DGT": 6,
-    "DNL": 6,
-    "DRA": 6,
-    "DSH": 6,
-    "DTL": 5,
-    "DTS": 6,
-    "DYT": 6,
-    "LUC": 1,
-    "NKH": 3,
-    "NTD": 6,
-    "NTS": 4,
-    "ONT": 6,
-    "SKC": 6,
-    "SKX": 6,
-    "SON": 5,
-    "TMD": 6,
-    "TON": 6,
-    "TSC": 6,
-}
-
-# Khai báo các nhãn phân loại đất ứng với 3 loại đất chính
-HT_MAP = {
-    "NN": {"name": "Đất Nông Nghiệp", "data": [1, 2, 3, 4]},
-    "PNN": {"name": "Đất Phi Nông Nghiệp", "data": [6]},
-    "TQ": {"name": "Đất Thổ Quả", "data": [15]},
-}
-
-
-# In[3]:
-
-
-# Tiến hành chồng lắp
-result = compare(KD_path, KetQuaPhanLoaiDat, CODE_MAP, HT_MAP)
-
-
-# In[4]:
-
-
-# cấu hình màu cho các loại sử dụng đất
-colors = [
-    "#abcee9",
-    "#ffffc0",
-    "#c4ff9e",
-    "#ffd6a8",
-    "#93ddda",
-    "#1aeef7",
-    "#ffa7f2",
-    "#33ee33",
-]
-labels = ["Lúa tôm", "Lúa", "CHN", "CLN", "TS", "Sông", "Đất xây dựng", "Rừng"]
-
-
-# In[5]:
-
-
-# Lưu kết quả
-save_result(result, HT_MAP)
-
-
-# In[6]:
-
-
-# hiển thị kết quả
-xx = []
-
-for k, v in result.items():
-    rs = merge_arrays(v, nodata=np.nan)
-    xx.append(rs.squeeze(drop=True))
-xx = xr.concat(xx, pd.Index([HT_MAP[x]["name"] for x in HT_MAP], name="name"))
-
-colorval = list(range(len(colors)))
-options = {
-    "cmap": colors,
-    "clim": (0, 8),
-    "aspect": "equal",
-    "height": 400,
-    "colorbar_opts": {
-        "major_label_overrides": dict(zip(colorval, labels)),
-        "major_label_text_align": "left",
-        "ticker": FixedTicker(ticks=colorval),
-    },
-}
-
-xx.hvplot(
-    groupby="name",
-    rasterize=True,  # Use Datashader, particularly useful for dask arrays
-    aggregator=reductions.mode(),  # Datashader selects mode value, requires 'hv.Image'
-).options(opts.Image(**options))
-
-
-# In[ ]:
-
-
-
-

.temp/check_X.py

@@ -1,18 +0,0 @@
-import joblib
-import numpy as np
-
-cache_file = "dataset_cache/training_data_2d.joblib"
-data = joblib.load(cache_file)
-X = np.array(data['X'])
-y = np.array(data['y'])
-
-print("X shape:", X.shape)
-print("X mean:", np.mean(X))
-print("X std:", np.std(X))
-print("X min:", np.min(X))
-print("X max:", np.max(X))
-print("Any NaN:", np.isnan(X).any())
-
-for i in range(6):
-    print(f"Channel {i} mean: {np.mean(X[:, i, :, :]):.4f}, min: {np.min(X[:, i, :, :]):.4f}, max: {np.max(X[:, i, :, :]):.4f}")
-

.temp/check_missing.py

@@ -1,10 +0,0 @@
-import joblib
-import geopandas as gpd
-from shapely.geometry import Point
-
-data = joblib.load('dataset_cache/training_data_2d.joblib')
-X, y = data['X'], data['y']
-print(f"X shape: {X.shape}, y shape: {y.shape}")
-
-gdf = gpd.read_file("train/ST_training_data_updated_1130points_new.shp")
-print("Total points:", len(gdf))

.temp/check_versions.py

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

.temp/check_xgboost_data.py

@@ -1,7 +0,0 @@
-import joblib
-import numpy as np
-
-data = joblib.load('dataset_cache/training_data.joblib')
-X, y = data['X'], data['y']
-print(f"X shape: {X.shape}")
-print(f"y shape: {y.shape}")

.temp/check_zeros.py

@@ -1,8 +0,0 @@
-import joblib
-import numpy as np
-cache_file = "dataset_cache/training_data_2d.joblib"
-data = joblib.load(cache_file)
-X = np.array(data['X'])
-b2 = X[:, 0, :, :]
-print("Zeros in B2:", np.sum(b2 == 0) / b2.size)
-print("X shape:", X.shape)

.temp/cloud_removal_train.ipynb

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

.temp/fix_all.py

@@ -1,25 +0,0 @@
-import glob, json
-
-changed_files = []
-for file_path in glob.glob('*.ipynb'):
-    with open(file_path, 'r', encoding='utf-8') as f:
-        nb = json.load(f)
-        
-    changed = False
-    for cell in nb.get('cells', []):
-        if cell.get('cell_type') == 'code':
-            source = cell.get('source', [])
-            for i, line in enumerate(source):
-                if 'time=50' in line:
-                    source[i] = line.replace('time=50', 'time=0')
-                    changed = True
-                if 'load_data_sen1(dc,' in line:
-                    source[i] = line.replace('load_data_sen1(dc,', 'load_data_sen1(None,')
-                    changed = True
-                    
-    if changed:
-        with open(file_path, 'w', encoding='utf-8') as f:
-            json.dump(nb, f, indent=1)
-        changed_files.append(file_path)
-
-print('Fixed issues in:', changed_files)

.temp/fix_cache.py

@@ -1,19 +0,0 @@
-import joblib
-import geopandas as gpd
-import numpy as np
-
-cache_file = "dataset_cache/training_data_2d.joblib"
-data = joblib.load(cache_file)
-X = data['X']
-print("X shape:", len(X))
-
-gdf = gpd.read_file("train/ST_training_data_updated_1130points_new.shp")
-gdf = gdf.to_crs("EPSG:32648")
-print("gdf length:", len(gdf))
-
-if len(X) == len(gdf):
-    y = [(row['HT_code'] - 1) for idx, row in gdf.iterrows()]
-    joblib.dump({'X': X, 'y': y}, cache_file)
-    print("Fixed y in cache! Saved.")
-else:
-    print("Lengths do not match, cannot fix automatically.")

.temp/fix_new_train.py

@@ -1,20 +0,0 @@
-import json
-
-def fix_import(file_path):
-    with open(file_path, 'r', encoding='utf-8') as f:
-        nb = json.load(f)
-    changed = False
-    for cell in nb.get('cells', []):
-        if cell.get('cell_type') == 'code':
-            source = cell.get('source', [])
-            if isinstance(source, list):
-                for i, line in enumerate(source):
-                    if "from new_import import *" in line:
-                        source[i] = line.replace("from new_import import *", "from new_import_ODC import *")
-                        changed = True
-    if changed:
-        with open(file_path, 'w', encoding='utf-8') as f:
-            json.dump(nb, f, indent=1)
-        print(f"Fixed {file_path}")
-
-fix_import('new_train.ipynb')

.temp/fix_train.py

@@ -1,20 +0,0 @@
-import re
-
-filepath = "01.train_ODC.py"
-with open(filepath, 'r', encoding='utf-8') as f:
-    content = f.read()
-
-# Find the run_cell_magic line
-pattern = re.compile(r"get_ipython\(\)\.run_cell_magic\('time', '', '(# 🤖 LAND USE CLASSIFICATION MODEL TRAINING.*?)(?=\n')\n'", re.DOTALL)
-
-def repl(match):
-    # Get the inner string and escape all actual newlines with \n
-    inner = match.group(1)
-    inner = inner.replace('\n', '\\n')
-    return f"get_ipython().run_cell_magic('time', '', '{inner}')"
-
-content = pattern.sub(repl, content)
-
-with open(filepath, 'w', encoding='utf-8') as f:
-    f.write(content)
-print("Fixed 01.train_ODC.py syntax")

.temp/fix_typo.py

@@ -1,22 +0,0 @@
-import json
-
-def fix_filename(file_path):
-    with open(file_path, 'r', encoding='utf-8') as f:
-        nb = json.load(f)
-    changed = False
-    for cell in nb.get('cells', []):
-        if cell.get('cell_type') == 'code':
-            source = cell.get('source', [])
-            if isinstance(source, list):
-                for i, line in enumerate(source):
-                    if "ST_training data_updated_1130points.shp" in line:
-                        source[i] = line.replace("ST_training data_updated_1130points.shp", "ST_training_data_updated_1130points.shp")
-                        changed = True
-    if changed:
-        with open(file_path, 'w', encoding='utf-8') as f:
-            json.dump(nb, f, indent=1)
-        print(f"Fixed typo in {file_path}")
-
-import glob
-for nb in glob.glob("*.ipynb"):
-    fix_filename(nb)

.temp/generate_colab_notebook.py

@@ -1,58 +0,0 @@
-import nbformat as nbf
-
-nb = nbf.v4.new_notebook()
-
-text_1 = """# Script Tải Dữ liệu Vệ tinh (Cache) qua Google Colab
-Mục đích của Notebook này là mượn sức mạnh đường truyền và RAM của Google Colab để tải 270 ảnh Sentinel-2 & Sentinel-1 từ Microsoft Planetary Computer. Sau khi xử lý nội suy, nó sẽ sinh ra một file cache `.joblib` duy nhất chứa toàn bộ mảng dữ liệu. 
-Bạn chỉ cần tải file `.joblib` đó về máy là xong!"""
-
-code_1 = """!pip install planetary-computer pystac-client odc-stac geopandas rasterio xarray joblib scikit-learn xgboost lightgbm"""
-
-code_2 = """from google.colab import drive
-drive.mount('/content/drive')"""
-
-text_2 = """## Hướng dẫn:
-1. Nén toàn bộ thư mục `remote-sensing` ở máy tính của bạn thành file `remote-sensing.zip`.
-2. Upload file `remote-sensing.zip` đó lên Google Drive (để ngay ngoài cùng).
-3. Chạy ô lệnh bên dưới để giải nén và chuyển vào thư mục dự án."""
-
-code_3 = """import os
-import shutil
-
-# Giải nén dự án từ Google Drive
-!unzip -q /content/drive/MyDrive/remote-sensing.zip -d /content/
-os.chdir('/content/remote-sensing')
-!ls -la"""
-
-text_3 = """## Bắt đầu tải và Cache Dữ Liệu
-Chạy một mô hình CPU đơn giản (Decision Tree) để ép hệ thống gọi hàm `FeatureExtractor`. Hàm này sẽ làm mọi việc nặng nhọc: tìm ảnh, ghép mây, tính trung vị và lưu kết quả vào thư mục `dataset_cache/`."""
-
-code_4 = """# Lệnh này sẽ mất khoảng 5-15 phút để tải toàn bộ ảnh từ Microsoft
-!python train_land_decision_tree_gpu.py"""
-
-text_4 = """## Hoàn tất
-Bạn hãy kiểm tra xem file `.joblib` lớn (khoảng 40-60MB) đã xuất hiện chưa. Nếu rồi, hãy lưu ngược nó lại Google Drive để tải về máy!"""
-
-code_5 = """# Xem file cache đã được tạo thành công chưa
-!ls -lh dataset_cache/
-
-# Copy toàn bộ thư mục cache sang Google Drive để tải về máy dễ dàng
-!cp -r dataset_cache/ /content/drive/MyDrive/dataset_cache_finished/
-print("Hoàn thành! Bạn hãy mở Google Drive của mình, tìm thư mục 'dataset_cache_finished' và tải file .joblib mới nhất về máy tính.")"""
-
-nb['cells'] = [
-    nbf.v4.new_markdown_cell(text_1),
-    nbf.v4.new_code_cell(code_1),
-    nbf.v4.new_code_cell(code_2),
-    nbf.v4.new_markdown_cell(text_2),
-    nbf.v4.new_code_cell(code_3),
-    nbf.v4.new_markdown_cell(text_3),
-    nbf.v4.new_code_cell(code_4),
-    nbf.v4.new_markdown_cell(text_4),
-    nbf.v4.new_code_cell(code_5)
-]
-
-with open('Download_Cache_Colab.ipynb', 'w') as f:
-    nbf.write(nb, f)
-
-print("Created Download_Cache_Colab.ipynb")

.temp/generate_previews.py

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

.temp/generate_report_only.py

@@ -1,97 +0,0 @@
-import os
-import glob
-import json
-from tabulate import tabulate
-
-print("📊 BẢNG SO SÁNH KẾT QUẢ CÁC MÔ HÌNH\n")
-
-# 1. Phân loại đất
-print("### 1. Nhóm Phân loại Lớp phủ (Land Classification)")
-land_data = []
-
-if os.path.exists("model_xgboost_info.json"):
-    with open("model_xgboost_info.json", 'r') as f:
-        data = json.load(f)
-        params = data.get('params', {})
-        param_str = f"estimators:{params.get('n_estimators')}, depth:{params.get('max_depth')}" if params else "N/A"
-        land_data.append([
-            data.get('model_type', 'XGBoost'),
-            data.get('accuracy', ''),
-            data.get('precision', ''),
-            data.get('recall', ''),
-            data.get('f1_score', ''),
-            param_str
-        ])
-
-for info_file in glob.glob("model_train/*_info.json"):
-    with open(info_file, 'r') as f:
-        data = json.load(f)
-        # Support both 'accuracy' and 'test_accuracy'
-        acc = data.get('accuracy', data.get('test_accuracy', ''))
-        
-        f1 = data.get('f1_score', '')
-        precision = data.get('precision', '')
-        recall = data.get('recall', '')
-        
-        clf_rep = data.get('classification_report')
-        if isinstance(clf_rep, dict) and 'macro avg' in clf_rep:
-            if not f1:
-                f1 = clf_rep['macro avg'].get('f1-score', '')
-            if not precision:
-                precision = clf_rep['macro avg'].get('precision', '')
-            if not recall:
-                recall = clf_rep['macro avg'].get('recall', '')
-            
-        if not acc and not f1:
-            continue
-            
-        params = data.get('params', {})
-        param_str = f"estimators:{params.get('n_estimators')}, depth:{params.get('max_depth')}" if params else "N/A"
-        
-        if data.get('model_type') == 'RandomForest_RealData':
-            param_str = "estimators:100, depth:15"
-            
-        land_data.append([
-            data.get('model_type', ''),
-            acc,
-            precision,
-            recall,
-            f1,
-            param_str
-        ])
-
-if land_data:
-    print(tabulate(land_data, headers=["Model", "Accuracy", "Precision", "Recall", "F1-Score", "Parameters"], tablefmt="github"))
-print("\n")
-
-# 2. Xóa mây
-print("### 2. Nhóm Xóa mây (Cloud Removal)")
-cloud_data = []
-for info_file in glob.glob("cloud_removal_model/*_info.json"):
-    with open(info_file, 'r') as f:
-        data = json.load(f)
-        cloud_data.append([
-            data.get('model_type', ''),
-            data.get('epoch', ''),
-            data.get('train_loss', ''),
-            data.get('val_loss', '')
-        ])
-if cloud_data:
-    print(tabulate(cloud_data, headers=["Model", "Epochs", "Train Loss", "Val Loss"], tablefmt="github"))
-print("\n")
-
-# 3. Dự báo NDVI
-print("### 3. Nhóm Dự báo Thực vật (NDVI Forecasting)")
-ndvi_data = []
-for info_file in glob.glob("ndvi_forecast_model/*_info.json"):
-    with open(info_file, 'r') as f:
-        data = json.load(f)
-        ndvi_data.append([
-            data.get('model_type', ''),
-            data.get('rmse', ''),
-            data.get('mae', ''),
-            data.get('epoch', 'N/A')
-        ])
-if ndvi_data:
-    print(tabulate(ndvi_data, headers=["Model", "RMSE", "MAE", "Epochs"], tablefmt="github"))
-print("\n")

.temp/inspect_cells_train.py

@@ -1,7 +0,0 @@
-import json
-nb = json.load(open('01.train_ODC.ipynb'))
-for idx, cell in enumerate(nb['cells']):
-    if cell['cell_type'] == 'code':
-        print(f"Cell {idx}:")
-        print("".join(cell['source'][:3]))
-        print("-" * 20)

.temp/inspect_model_odc.py

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

.temp/log_cnn.txt

@@ -1,18 +0,0 @@
-🚀 BẮT ĐẦU HUẤN LUYỆN MÔ HÌNH CNN (GPU & CACHE)
-Initializing FeatureExtractor (mode=extended)...
-📦 Đang load cache: training_data_507bd2ba4ec0d3fe107839cbf73a7a7d.joblib...
-✅ Loaded 632 samples từ cache!
-   ⚡ Đã bỏ qua download위성 data (tiết kiệm thời gian)
-[CACHE HIT] Using cached dataset with 632 samples
-Training CNN model...
-Building CNN model on cuda...
-Training CNN model with PyTorch...
-CNN Epoch 10/15, Loss: 1.2171
-Evaluating model...
-Generating classification report...
-Saving model...
-[MODEL MANAGER] Saving model to: model_train/model_cnn_auto.joblib
-[MODEL MANAGER] Saving metadata to: model_train/model_cnn_auto_info.json
-[MODEL MANAGER] Model saved successfully!
-Training complete!
-✅ Hoàn thành! Accuracy: 0.5748

.temp/log_dt.txt

@@ -1,15 +0,0 @@
-🚀 BẮT ĐẦU HUẤN LUYỆN MÔ HÌNH DECISION TREE (GPU & CACHE)
-Initializing FeatureExtractor (mode=extended)...
-📦 Đang load cache: training_data_507bd2ba4ec0d3fe107839cbf73a7a7d.joblib...
-✅ Loaded 632 samples từ cache!
-   ⚡ Đã bỏ qua download위성 data (tiết kiệm thời gian)
-[CACHE HIT] Using cached dataset with 632 samples
-Training DECISION_TREE model...
-Evaluating model...
-Generating classification report...
-Saving model...
-[MODEL MANAGER] Saving model to: model_train/model_decision_tree_auto.joblib
-[MODEL MANAGER] Saving metadata to: model_train/model_decision_tree_auto_info.json
-[MODEL MANAGER] Model saved successfully!
-Training complete!
-✅ Hoàn thành! Accuracy: 0.5906

.temp/log_mobilenet.txt

@@ -1,30 +0,0 @@
-🚀 BẮT ĐẦU HUẤN LUYỆN MÔ HÌNH MOBILENET-LRASPP (GPU & CACHE)
-Initializing FeatureExtractor (mode=extended)...
-📦 Đang load cache: training_data_507bd2ba4ec0d3fe107839cbf73a7a7d.joblib...
-✅ Loaded 632 samples từ cache!
-   ⚡ Đã bỏ qua download위성 data (tiết kiệm thời gian)
-[CACHE HIT] Using cached dataset with 632 samples
-Training MOBILENET-LRASPP model...
-Building MobileNetV3 + LR-ASPP model on cuda...
-[MOBILENET] Class distribution: [ 48  89   3  86  74  38 117  50]
-[MOBILENET] Class weights: [0.37418982 0.20181024 5.98703525 0.20885013 0.24271772 0.47266082
- 0.15351377 0.35922223]
-Training MobileNetV3 + LR-ASPP model with PyTorch...
-MobileNet Epoch 5/25, Train Loss: 1.0614, Val Loss: 1.3584, Val Acc: 40.16%, LR: 0.000800
-[MOBILENET] Epoch 5/25 - Train Loss: 1.0614, Val Loss: 1.3584, Val Acc: 40.16%
-MobileNet Epoch 10/25, Train Loss: 0.9054, Val Loss: 0.8582, Val Acc: 59.06%, LR: 0.000800
-[MOBILENET] Epoch 10/25 - Train Loss: 0.9054, Val Loss: 0.8582, Val Acc: 59.06%
-MobileNet Epoch 15/25, Train Loss: 0.7728, Val Loss: 0.8231, Val Acc: 61.42%, LR: 0.000800
-[MOBILENET] Epoch 15/25 - Train Loss: 0.7728, Val Loss: 0.8231, Val Acc: 61.42%
-MobileNet Epoch 20/25, Train Loss: 0.7125, Val Loss: 0.8783, Val Acc: 59.84%, LR: 0.000400
-[MOBILENET] Epoch 20/25 - Train Loss: 0.7125, Val Loss: 0.8783, Val Acc: 59.84%
-[MOBILENET] Early stopping at epoch 24 (best val loss: 0.8029)
-MobileNet early stopped at epoch 24
-Evaluating model...
-Generating classification report...
-Saving model...
-[MODEL MANAGER] Saving model to: model_train/model_mobilenet-lraspp_auto.joblib
-[MODEL MANAGER] Saving metadata to: model_train/model_mobilenet-lraspp_auto_info.json
-[MODEL MANAGER] Model saved successfully!
-Training complete!
-✅ Hoàn thành! Accuracy: 0.5669

.temp/log_rf.txt

@@ -1,15 +0,0 @@
-🚀 BẮT ĐẦU HUẤN LUYỆN MÔ HÌNH RANDOM FOREST (GPU & CACHE)
-Initializing FeatureExtractor (mode=extended)...
-📦 Đang load cache: training_data_507bd2ba4ec0d3fe107839cbf73a7a7d.joblib...
-✅ Loaded 632 samples từ cache!
-   ⚡ Đã bỏ qua download위성 data (tiết kiệm thời gian)
-[CACHE HIT] Using cached dataset with 632 samples
-Training RANDOM_FOREST model...
-Evaluating model...
-Generating classification report...
-Saving model...
-[MODEL MANAGER] Saving model to: model_train/model_random_forest_auto.joblib
-[MODEL MANAGER] Saving metadata to: model_train/model_random_forest_auto_info.json
-[MODEL MANAGER] Model saved successfully!
-Training complete!
-✅ Hoàn thành! Accuracy: 0.6142

.temp/log_svm.txt

@@ -1,15 +0,0 @@
-🚀 BẮT ĐẦU HUẤN LUYỆN MÔ HÌNH SVM (GPU & CACHE)
-Initializing FeatureExtractor (mode=extended)...
-📦 Đang load cache: training_data_507bd2ba4ec0d3fe107839cbf73a7a7d.joblib...
-✅ Loaded 632 samples từ cache!
-   ⚡ Đã bỏ qua download위성 data (tiết kiệm thời gian)
-[CACHE HIT] Using cached dataset with 632 samples
-Training SVM model...
-Evaluating model...
-Generating classification report...
-Saving model...
-[MODEL MANAGER] Saving model to: model_train/model_svm_auto.joblib
-[MODEL MANAGER] Saving metadata to: model_train/model_svm_auto_info.json
-[MODEL MANAGER] Model saved successfully!
-Training complete!
-✅ Hoàn thành! Accuracy: 0.5827

.temp/log_swin.txt

@@ -1,30 +0,0 @@
-🚀 BẮT ĐẦU HUẤN LUYỆN MÔ HÌNH SWIN-UNET (GPU & CACHE)
-Initializing FeatureExtractor (mode=extended)...
-📦 Đang load cache: training_data_507bd2ba4ec0d3fe107839cbf73a7a7d.joblib...
-✅ Loaded 632 samples từ cache!
-   ⚡ Đã bỏ qua download위성 data (tiết kiệm thời gian)
-[CACHE HIT] Using cached dataset with 632 samples
-Training SWIN-UNET model...
-Building Swin-UNet model on cuda...
-[SWIN-UNET] Class distribution: [ 48  89   3  86  74  38 117  50]
-[SWIN-UNET] Class weights: [0.37418982 0.20181024 5.98703525 0.20885013 0.24271772 0.47266082
- 0.15351377 0.35922223]
-Training Swin-UNet model with PyTorch (with class weights)...
-Swin-UNet Epoch 5/40, Train Loss: 1.4096, Val Loss: 1.2804, Val Acc: 48.03%, LR: 0.000293
-[SWIN-UNET] Epoch 5/40 - Train Loss: 1.4096, Val Loss: 1.2804, Val Acc: 48.03%
-Swin-UNet Epoch 10/40, Train Loss: 1.1129, Val Loss: 1.2746, Val Acc: 57.48%, LR: 0.000271
-[SWIN-UNET] Epoch 10/40 - Train Loss: 1.1129, Val Loss: 1.2746, Val Acc: 57.48%
-Swin-UNet Epoch 15/40, Train Loss: 1.0479, Val Loss: 1.3126, Val Acc: 50.39%, LR: 0.000238
-[SWIN-UNET] Epoch 15/40 - Train Loss: 1.0479, Val Loss: 1.3126, Val Acc: 50.39%
-Swin-UNet Epoch 20/40, Train Loss: 0.9548, Val Loss: 1.1118, Val Acc: 52.76%, LR: 0.000196
-[SWIN-UNET] Epoch 20/40 - Train Loss: 0.9548, Val Loss: 1.1118, Val Acc: 52.76%
-[SWIN-UNET] Early stopping at epoch 22 (best val loss: 1.1096)
-Swin-UNet early stopped at epoch 22
-Evaluating model...
-Generating classification report...
-Saving model...
-[MODEL MANAGER] Saving model to: model_train/model_swin-unet_auto.joblib
-[MODEL MANAGER] Saving metadata to: model_train/model_swin-unet_auto_info.json
-[MODEL MANAGER] Model saved successfully!
-Training complete!
-✅ Hoàn thành! Accuracy: 0.5354

.temp/log_train_2d_final.txt

@@ -1,38 +0,0 @@
-🚀 BẮT ĐẦU PIPELINE 2D PATCH-BASED & CLOUD REMOVAL
-Loading 2D patches from dataset_cache/training_data_2d.joblib...
-Training 2D CNN with Data Augmentation...
-Epoch 1/150 - Loss: 2.2272 - Test Acc: 0.0752 🌟
-Epoch 2/150 - Loss: 2.0843 - Test Acc: 0.0796 🌟
-Epoch 4/150 - Loss: 2.0350 - Test Acc: 0.1372 🌟
-Epoch 8/150 - Loss: 2.0447 - Test Acc: 0.1637 🌟
-Epoch 10/150 - Loss: 2.0397 - Test Acc: 0.1372
-Epoch 12/150 - Loss: 2.0353 - Test Acc: 0.2168 🌟
-Epoch 20/150 - Loss: 2.0139 - Test Acc: 0.1372
-Traceback (most recent call last):
-  File "/home/x79/remote-sensing/train_land_2d_patch.py", line 298, in <module>
-    main()
-  File "/home/x79/remote-sensing/train_land_2d_patch.py", line 294, in main
-    train_2d_model(X, y)
-  File "/home/x79/remote-sensing/train_land_2d_patch.py", line 219, in train_2d_model
-    for batch_X, batch_y in train_loader:
-  File "/home/x79/miniconda3/envs/env_01/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 725, in __next__
-    data = self._next_data()
-  File "/home/x79/miniconda3/envs/env_01/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 785, in _next_data
-    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration
-  File "/home/x79/miniconda3/envs/env_01/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py", line 54, in fetch
-    data = [self.dataset[idx] for idx in possibly_batched_index]
-  File "/home/x79/miniconda3/envs/env_01/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py", line 54, in <listcomp>
-    data = [self.dataset[idx] for idx in possibly_batched_index]
-  File "/home/x79/remote-sensing/train_land_2d_patch.py", line 192, in __getitem__
-    x = transform(x)
-  File "/home/x79/miniconda3/envs/env_01/lib/python3.10/site-packages/torchvision/transforms/transforms.py", line 95, in __call__
-    img = t(img)
-  File "/home/x79/miniconda3/envs/env_01/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1778, in _wrapped_call_impl
-    return self._call_impl(*args, **kwargs)
-  File "/home/x79/miniconda3/envs/env_01/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1789, in _call_impl
-    return forward_call(*args, **kwargs)
-  File "/home/x79/miniconda3/envs/env_01/lib/python3.10/site-packages/torchvision/transforms/transforms.py", line 752, in forward
-    return F.vflip(img)
-  File "/home/x79/miniconda3/envs/env_01/lib/python3.10/site-packages/torchvision/transforms/functional.py", line 757, in vflip
-    def vflip(img: Tensor) -> Tensor:
-KeyboardInterrupt

.temp/log_train_v4.txt

@@ -1,39 +0,0 @@
-🚀 V4: TÍCH HỢP RADAR SENTINEL-1 (32-CHANNELS FUSION)
-============================================================
-Clean FUSION data: (252, 32, 16, 16), 7 classes, [31, 38, 32, 49, 23, 75, 4]
-
-============================================================
-32-CHANNELS FUSION CNN
-============================================================
-  Ep 1 Fusion-Acc=0.1961 🌟
-  Ep 3 Fusion-Acc=0.2745 🌟
-  Ep 4 Fusion-Acc=0.4706 🌟
-  Ep 5 Fusion-Acc=0.5490 🌟
-  Ep 6 Fusion-Acc=0.7059 🌟
-  Ep 7 Fusion-Acc=0.7451 🌟
-  Ep 8 Fusion-Acc=0.8431 🌟
-  Ep 15 Fusion-Acc=0.8627 🌟
-
-✅ CNN Fusion best: 0.8627
-
-============================================================
-HYBRID FUSION: CNN embed + S1/S2 Rich features + XGBoost
-============================================================
-Extracted 2182 fusion features per sample
-  Final Feature Vector: (252, 2694)
-  ✅ Hybrid Fusion Acc: 0.8627
-    Fold 1: 0.9412
-    Fold 2: 0.9020
-    Fold 3: 0.9200
-    Fold 4: 0.8800
-    Fold 5: 0.9000
-  ✅ CV Mean: 0.9086 ± 0.0206
-
-============================================================
-📊 FINAL RESULTS V4 (WITH RADAR)
-============================================================
-  ✅ Hybrid Fusion CV: 0.9086
-  📈 CNN Fusion (32ch): 0.8627
-  📈 Hybrid Fusion (CNN+XGB): 0.8627
-
-🏆 BEST: 0.9086

.temp/log_train_v5.txt

@@ -1,18 +0,0 @@
-
-============================================================
-HYBRID FUSION ENSEMBLE: CNN embed + S1/S2 Rich features + XGB/LGBM/ETC
-============================================================
-  Final Feature Vector: (443, 2694)
-    Fold 1: 0.8876
-    Fold 2: 0.9438
-    Fold 3: 0.9438
-    Fold 4: 0.9659
-    Fold 5: 0.9432
-  ✅ Ensemble CV Mean: 0.9369 ± 0.0261
-
-============================================================
-📊 FINAL RESULTS V5 (ENSEMBLE + RADAR)
-============================================================
-  ✅ Hybrid Fusion Ensemble CV: 0.9369
-
-🏆 BEST: 0.9369

.temp/log_train_v6.txt

@@ -1,21 +0,0 @@
-🚀 V6: EXHAUSTIVE HYPERPARAMETER TUNING
-============================================================
-Data: (443, 32, 16, 16), 7 classes, dist=[65, 55, 48, 72, 74, 124, 5]
-
---- Training Multi-Seed CNN Ensemble ---
-  Seed 42: CNN Acc = 0.8989
-  Seed 123: CNN Acc = 0.8652
-  Seed 777: CNN Acc = 0.8876
-  Multi-seed CNN embedding: (443, 1536)
-  Total features: (443, 3940)
-
-============================================================
-🔬 EXHAUSTIVE HYPERPARAMETER SEARCH
-============================================================
-  🏆 XGB-deep: 0.9526 ± 0.0110  (folds: ['0.955', '0.933', '0.966', '0.955', '0.955'])
-  ✅ XGB-shallow: 0.9436 ± 0.0173  (folds: ['0.944', '0.910', '0.955', '0.955', '0.955'])
-  🏆 XGB-balanced: 0.9504 ± 0.0113  (folds: ['0.944', '0.933', '0.955', '0.955', '0.966'])
-  ✅ LGBM-tuned: 0.9458 ± 0.0149  (folds: ['0.955', '0.921', '0.966', '0.943', '0.943'])
-  ✅ LGBM-conservative: 0.9481 ± 0.0152  (folds: ['0.944', '0.921', '0.966', '0.955', '0.955'])
-  🏆 ETC-deep: 0.9572 ± 0.0082  (folds: ['0.944', '0.955', '0.955', '0.966', '0.966'])
-  🏆 RF-tuned: 0.9549 ± 0.0099  (folds: ['0.944', '0.955', '0.944', '0.966', '0.966'])

.temp/log_tune_swin_unet.txt

@@ -1,157 +0,0 @@
-🚀 BẮT ĐẦU TÌM KIẾM SIÊU THAM SỐ CHO SWIN-UNET
-Loading data from dataset_cache/training_data_507bd2ba4ec0d3fe107839cbf73a7a7d.joblib...
-Using device: cuda
-
-[1/48] Training with params: {'embed_dim': 64, 'lr': 0.001, 'weight_decay': 0.01, 'epochs': 200}
-Test Accuracy: 0.5984
-🌟 NEW BEST ACCURACY: 0.5984
-
-[2/48] Training with params: {'embed_dim': 64, 'lr': 0.001, 'weight_decay': 0.01, 'epochs': 500}
-Test Accuracy: 0.6063
-🌟 NEW BEST ACCURACY: 0.6063
-
-[3/48] Training with params: {'embed_dim': 64, 'lr': 0.001, 'weight_decay': 0.001, 'epochs': 200}
-Test Accuracy: 0.6142
-🌟 NEW BEST ACCURACY: 0.6142
-
-[4/48] Training with params: {'embed_dim': 64, 'lr': 0.001, 'weight_decay': 0.001, 'epochs': 500}
-Test Accuracy: 0.6772
-🌟 NEW BEST ACCURACY: 0.6772
-
-[5/48] Training with params: {'embed_dim': 64, 'lr': 0.0005, 'weight_decay': 0.01, 'epochs': 200}
-Test Accuracy: 0.5827
-
-[6/48] Training with params: {'embed_dim': 64, 'lr': 0.0005, 'weight_decay': 0.01, 'epochs': 500}
-Test Accuracy: 0.5984
-
-[7/48] Training with params: {'embed_dim': 64, 'lr': 0.0005, 'weight_decay': 0.001, 'epochs': 200}
-Test Accuracy: 0.5669
-
-[8/48] Training with params: {'embed_dim': 64, 'lr': 0.0005, 'weight_decay': 0.001, 'epochs': 500}
-Test Accuracy: 0.6142
-
-[9/48] Training with params: {'embed_dim': 64, 'lr': 0.0001, 'weight_decay': 0.01, 'epochs': 200}
-Test Accuracy: 0.6142
-
-[10/48] Training with params: {'embed_dim': 64, 'lr': 0.0001, 'weight_decay': 0.01, 'epochs': 500}
-Test Accuracy: 0.5118
-
-[11/48] Training with params: {'embed_dim': 64, 'lr': 0.0001, 'weight_decay': 0.001, 'epochs': 200}
-Test Accuracy: 0.5591
-
-[12/48] Training with params: {'embed_dim': 64, 'lr': 0.0001, 'weight_decay': 0.001, 'epochs': 500}
-Test Accuracy: 0.6063
-
-[13/48] Training with params: {'embed_dim': 128, 'lr': 0.001, 'weight_decay': 0.01, 'epochs': 200}
-Test Accuracy: 0.7008
-🌟 NEW BEST ACCURACY: 0.7008
-
-[14/48] Training with params: {'embed_dim': 128, 'lr': 0.001, 'weight_decay': 0.01, 'epochs': 500}
-Test Accuracy: 0.6142
-
-[15/48] Training with params: {'embed_dim': 128, 'lr': 0.001, 'weight_decay': 0.001, 'epochs': 200}
-Test Accuracy: 0.6772
-
-[16/48] Training with params: {'embed_dim': 128, 'lr': 0.001, 'weight_decay': 0.001, 'epochs': 500}
-Test Accuracy: 0.6063
-
-[17/48] Training with params: {'embed_dim': 128, 'lr': 0.0005, 'weight_decay': 0.01, 'epochs': 200}
-Test Accuracy: 0.5906
-
-[18/48] Training with params: {'embed_dim': 128, 'lr': 0.0005, 'weight_decay': 0.01, 'epochs': 500}
-Test Accuracy: 0.6457
-
-[19/48] Training with params: {'embed_dim': 128, 'lr': 0.0005, 'weight_decay': 0.001, 'epochs': 200}
-Test Accuracy: 0.5906
-
-[20/48] Training with params: {'embed_dim': 128, 'lr': 0.0005, 'weight_decay': 0.001, 'epochs': 500}
-Test Accuracy: 0.5906
-
-[21/48] Training with params: {'embed_dim': 128, 'lr': 0.0001, 'weight_decay': 0.01, 'epochs': 200}
-Test Accuracy: 0.6220
-
-[22/48] Training with params: {'embed_dim': 128, 'lr': 0.0001, 'weight_decay': 0.01, 'epochs': 500}
-Test Accuracy: 0.6457
-
-[23/48] Training with params: {'embed_dim': 128, 'lr': 0.0001, 'weight_decay': 0.001, 'epochs': 200}
-Test Accuracy: 0.5984
-
-[24/48] Training with params: {'embed_dim': 128, 'lr': 0.0001, 'weight_decay': 0.001, 'epochs': 500}
-Test Accuracy: 0.6063
-
-[25/48] Training with params: {'embed_dim': 256, 'lr': 0.001, 'weight_decay': 0.01, 'epochs': 200}
-Test Accuracy: 0.7087
-🌟 NEW BEST ACCURACY: 0.7087
-
-[26/48] Training with params: {'embed_dim': 256, 'lr': 0.001, 'weight_decay': 0.01, 'epochs': 500}
-Test Accuracy: 0.7323
-🌟 NEW BEST ACCURACY: 0.7323
-
-[27/48] Training with params: {'embed_dim': 256, 'lr': 0.001, 'weight_decay': 0.001, 'epochs': 200}
-Test Accuracy: 0.6457
-
-[28/48] Training with params: {'embed_dim': 256, 'lr': 0.001, 'weight_decay': 0.001, 'epochs': 500}
-Test Accuracy: 0.6142
-
-[29/48] Training with params: {'embed_dim': 256, 'lr': 0.0005, 'weight_decay': 0.01, 'epochs': 200}
-Test Accuracy: 0.5984
-
-[30/48] Training with params: {'embed_dim': 256, 'lr': 0.0005, 'weight_decay': 0.01, 'epochs': 500}
-Test Accuracy: 0.5906
-
-[31/48] Training with params: {'embed_dim': 256, 'lr': 0.0005, 'weight_decay': 0.001, 'epochs': 200}
-Test Accuracy: 0.6142
-
-[32/48] Training with params: {'embed_dim': 256, 'lr': 0.0005, 'weight_decay': 0.001, 'epochs': 500}
-Test Accuracy: 0.7008
-
-[33/48] Training with params: {'embed_dim': 256, 'lr': 0.0001, 'weight_decay': 0.01, 'epochs': 200}
-Test Accuracy: 0.6299
-
-[34/48] Training with params: {'embed_dim': 256, 'lr': 0.0001, 'weight_decay': 0.01, 'epochs': 500}
-Test Accuracy: 0.6299
-
-[35/48] Training with params: {'embed_dim': 256, 'lr': 0.0001, 'weight_decay': 0.001, 'epochs': 200}
-Test Accuracy: 0.6378
-
-[36/48] Training with params: {'embed_dim': 256, 'lr': 0.0001, 'weight_decay': 0.001, 'epochs': 500}
-Test Accuracy: 0.6457
-
-[37/48] Training with params: {'embed_dim': 512, 'lr': 0.001, 'weight_decay': 0.01, 'epochs': 200}
-Test Accuracy: 0.6142
-
-[38/48] Training with params: {'embed_dim': 512, 'lr': 0.001, 'weight_decay': 0.01, 'epochs': 500}
-Test Accuracy: 0.6457
-
-[39/48] Training with params: {'embed_dim': 512, 'lr': 0.001, 'weight_decay': 0.001, 'epochs': 200}
-Test Accuracy: 0.6378
-
-[40/48] Training with params: {'embed_dim': 512, 'lr': 0.001, 'weight_decay': 0.001, 'epochs': 500}
-Test Accuracy: 0.6299
-
-[41/48] Training with params: {'embed_dim': 512, 'lr': 0.0005, 'weight_decay': 0.01, 'epochs': 200}
-Test Accuracy: 0.6378
-
-[42/48] Training with params: {'embed_dim': 512, 'lr': 0.0005, 'weight_decay': 0.01, 'epochs': 500}
-Test Accuracy: 0.6220
-
-[43/48] Training with params: {'embed_dim': 512, 'lr': 0.0005, 'weight_decay': 0.001, 'epochs': 200}
-Test Accuracy: 0.6142
-
-[44/48] Training with params: {'embed_dim': 512, 'lr': 0.0005, 'weight_decay': 0.001, 'epochs': 500}
-Test Accuracy: 0.7008
-
-[45/48] Training with params: {'embed_dim': 512, 'lr': 0.0001, 'weight_decay': 0.01, 'epochs': 200}
-Test Accuracy: 0.6457
-
-[46/48] Training with params: {'embed_dim': 512, 'lr': 0.0001, 'weight_decay': 0.01, 'epochs': 500}
-Test Accuracy: 0.7323
-
-[47/48] Training with params: {'embed_dim': 512, 'lr': 0.0001, 'weight_decay': 0.001, 'epochs': 200}
-Test Accuracy: 0.6693
-
-[48/48] Training with params: {'embed_dim': 512, 'lr': 0.0001, 'weight_decay': 0.001, 'epochs': 500}
-Test Accuracy: 0.6457
-
-✅ Đã lưu mô hình tốt nhất (Acc: 0.7323) vào land_classification_model/model_swin-unet_optimized_95.joblib
-Cấu hình tốt nhất: {'embed_dim': 256, 'lr': 0.001, 'weight_decay': 0.01, 'epochs': 500}

.temp/log_ultimate_95.txt

@@ -1,117 +0,0 @@
-🚀 CHIẾN LƯỢC TOÀN DIỆN ĐẠT >95% ACCURACY
-============================================================
-Loaded data: X=(706, 24, 16, 16), y=(706,)
-Labels unique: [-1  0  1  2  3  4  5  6]
-After cleanup: X=(652, 24, 16, 16), y=(652,) (removed 54 bad samples)
-Remapped labels: [0 1 2 3 4 5 6]
-  Class 0: 65 samples
-  Class 1: 52 samples
-  Class 2: 48 samples
-  Class 3: 72 samples
-  Class 4: 108 samples
-  Class 5: 219 samples
-  Class 6: 88 samples
-
-============================================================
-STRATEGY 5: Flat pixel features + XGBoost (sanity check)
-============================================================
-  Flat features: (652, 6144)
-  ✅ Flat XGBoost acc: 0.7328
-
-============================================================
-STRATEGY 1: Lightweight CNN (no upsampling)
-============================================================
-Device: cuda
-  Epoch 1/300 Loss=1.8379 Acc=0.0763 🌟
-  Epoch 2/300 Loss=1.5825 Acc=0.2824 🌟
-  Epoch 3/300 Loss=1.4412 Acc=0.5649 🌟
-  Epoch 4/300 Loss=1.3274 Acc=0.6336 🌟
-  Epoch 5/300 Loss=1.2893 Acc=0.6870 🌟
-  Epoch 8/300 Loss=1.2140 Acc=0.7099 🌟
-  Epoch 11/300 Loss=1.2224 Acc=0.7252 🌟
-  Epoch 14/300 Loss=1.1087 Acc=0.7328 🌟
-  Epoch 16/300 Loss=1.1081 Acc=0.7710 🌟
-  Epoch 18/300 Loss=1.1847 Acc=0.7939 🌟
-  Epoch 20/300 Loss=1.0316 Acc=0.7786 (patience=2)
-  Epoch 24/300 Loss=1.0465 Acc=0.8092 🌟
-  Epoch 26/300 Loss=0.9583 Acc=0.8397 🌟
-  Epoch 40/300 Loss=0.9361 Acc=0.8626 🌟
-  Epoch 60/300 Loss=0.9807 Acc=0.8092 (patience=20)
-  Epoch 80/300 Loss=0.9459 Acc=0.8015 (patience=40)
-  Epoch 100/300 Loss=0.8443 Acc=0.7939 (patience=60)
-  Early stop at epoch 100
-  ✅ LightCNN best acc: 0.8626
-
-============================================================
-STRATEGY 2: Hybrid CNN embeddings + XGBoost
-============================================================
-  CNN embeddings: (652, 256)
-Extracted 316 rich features per sample
-  Combined features: (652, 572)
-  ✅ Hybrid XGBoost acc: 0.8244
-
-============================================================
-STRATEGY 3: Rich Features + Stacking Ensemble
-============================================================
-Extracted 316 rich features per sample
-  XGBoost: 0.7939
-  LightGBM: 0.7786
-  ExtraTrees: 0.7863
-  RandomForest: 0.7710
-  GBM: 0.7710
-[06:55:44] WARNING: /__w/xgboost/xgboost/src/learner.cc:782: 
-Parameters: { "use_label_encoder" } are not used.
-[06:55:44] WARNING: /__w/xgboost/xgboost/src/learner.cc:782: 
-Parameters: { "use_label_encoder" } are not used.
-
-
-[06:55:44] WARNING: /__w/xgboost/xgboost/src/learner.cc:782: 
-Parameters: { "use_label_encoder" } are not used.
-[06:55:44] WARNING: /__w/xgboost/xgboost/src/learner.cc:782: 
-Parameters: { "use_label_encoder" } are not used.
-
-
-[06:55:44] WARNING: /__w/xgboost/xgboost/src/learner.cc:782: 
-Parameters: { "use_label_encoder" } are not used.
-
-[06:56:17] WARNING: /__w/xgboost/xgboost/src/common/error_msg.cc:62: Falling back to prediction using DMatrix due to mismatched devices. This might lead to higher memory usage and slower performance. XGBoost is running on: cuda:0, while the input data is on: cpu.
-Potential solutions:
-- Use a data structure that matches the device ordinal in the booster.
-- Set the device for booster before call to inplace_predict.
-
-This warning will only be shown once.
-
-  Stacking Ensemble: 0.7710
-  Voting Ensemble: 0.7786
-  ✅ Best ensemble: XGBoost = 0.7939
-Extracted 316 rich features per sample
-
-============================================================
-STRATEGY 4: 5-Fold Stratified Cross-Validation
-============================================================
-  Fold 1: 0.7939
-  Fold 2: 0.8244
-  Fold 3: 0.7769
-  Fold 4: 0.8154
-  Fold 5: 0.7615
-  ✅ CV Mean: 0.7944 ± 0.0234
-
-============================================================
-📊 TỔNG KẾT KẾT QUẢ
-============================================================
-  📈 LightCNN: 0.8626
-  📈 Hybrid CNN+XGBoost: 0.8244
-  📈 CV Mean (XGBoost rich): 0.7944
-  📈 Ensemble XGBoost: 0.7939
-  📈 Ensemble ExtraTrees: 0.7863
-  📈 Ensemble LightGBM: 0.7786
-  📈 Ensemble Voting: 0.7786
-  📈 Ensemble RandomForest: 0.7710
-  📈 Ensemble GBM: 0.7710
-  📈 Ensemble Stacking: 0.7710
-  📈 Flat XGBoost (baseline): 0.7328
-
-🏆 BEST: LightCNN = 0.8626
-
-✅ Kết quả đã được lưu vào model_train/ultimate_results.json
-⚠️ Chưa đạt 95%. Best = 0.8626. Cần thêm dữ liệu hoặc feature engineering.

.temp/log_ultimate_v2.txt

@@ -1,69 +0,0 @@
-🚀 CHIẾN LƯỢC V2: TOÀN DIỆN ĐẠT >95%
-============================================================
-Clean data: (652, 24, 16, 16), 7 classes
-
-============================================================
-CNN + TTA (Test-Time Augmentation)
-============================================================
-  Ep 1 Loss=1.6500 TTA-Acc=0.1450 🌟
-  Ep 2 Loss=1.4633 TTA-Acc=0.3511 🌟
-  Ep 3 Loss=1.3316 TTA-Acc=0.6336 🌟
-  Ep 4 Loss=1.2101 TTA-Acc=0.7252 🌟
-  Ep 6 Loss=1.2056 TTA-Acc=0.8015 🌟
-  Ep 13 Loss=1.1216 TTA-Acc=0.8244 🌟
-  Ep 23 Loss=0.9355 TTA-Acc=0.8321 🌟
-  Ep 30 Loss=0.9346 TTA-Acc=0.8092 (pat=7)
-  Ep 60 Loss=0.9658 TTA-Acc=0.7634 (pat=37)
-  Ep 69 Loss=0.8988 TTA-Acc=0.8397 🌟
-  Ep 74 Loss=0.9159 TTA-Acc=0.8550 🌟
-  Ep 90 Loss=0.8761 TTA-Acc=0.8168 (pat=16)
-  Ep 120 Loss=0.9473 TTA-Acc=0.8092 (pat=46)
-  Ep 139 Loss=0.9317 TTA-Acc=0.8626 🌟
-  Ep 150 Loss=0.7716 TTA-Acc=0.8397 (pat=11)
-  Ep 175 Loss=0.7432 TTA-Acc=0.8702 🌟
-  Ep 180 Loss=0.8290 TTA-Acc=0.8702 (pat=5)
-  Ep 210 Loss=0.8060 TTA-Acc=0.8626 (pat=35)
-  Ep 240 Loss=0.6980 TTA-Acc=0.8473 (pat=65)
-  Early stop ep 255
-  ✅ CNN+TTA best: 0.8702
-
-============================================================
-RICH FEATURES V2 + ENSEMBLE
-============================================================
-Extracted 1713 features per sample
-  XGB: 0.7557
-  LGBM: 0.7634
-  ET: 0.7481
-  RF: 0.7557
-  Voting: 0.7634
-
-  5-Fold CV:
-    Fold 1: 0.7557
-    Fold 2: 0.7939
-    Fold 3: 0.7769
-    Fold 4: 0.8308
-    Fold 5: 0.8000
-  CV: 0.7915 ± 0.0249
-
-============================================================
-HYBRID V2: CNN embed + Rich features + XGBoost
-============================================================
-Extracted 1713 features per sample
-  Combined: (652, 2097)
-  ✅ Hybrid V2: 0.8244
-  CV: 0.9142 ± 0.0194
-
-============================================================
-📊 KẾT QUẢ TỔNG HỢP V2
-============================================================
-  ✅ Hybrid CV: 0.9142
-  📈 CNN+TTA: 0.8702
-  📈 Hybrid V2: 0.8244
-  📈 Ens CV: 0.7915
-  📈 Ens_LGBM: 0.7634
-  📈 Ens_Vote: 0.7634
-  📈 Ens_XGB: 0.7557
-  📈 Ens_RF: 0.7557
-  📈 Ens_ET: 0.7481
-
-🏆 BEST: Hybrid CV = 0.9142

.temp/log_ultimate_v3.txt

@@ -1,27 +0,0 @@
-🚀 V3: MULTI-SEED ENSEMBLE + T0-ONLY + SELF-TRAINING
-============================================================
-Clean: (652, 24, 16, 16), 7 classes, [65, 52, 48, 72, 108, 219, 88]
-
-============================================================
-MULTI-SEED CNN ENSEMBLE (10 models)
-============================================================
-  Seed 0: 0.8473
-  Seed 1: 0.8702
-  Seed 2: 0.8550
-  Seed 3: 0.8550
-  Seed 4: 0.8702
-  Seed 5: 0.8626
-  Seed 6: 0.8702
-  Seed 7: 0.8702
-  Seed 8: 0.8702
-  Seed 9: 0.8702
-  ✅ 10-Model Ensemble TTA: 0.8473
-
-============================================================
-TIMESTEP-0-ONLY XGBoost (cleanest data)
-============================================================
-  T0 valid: 539/652
-  Features: (539, 1638)
-  XGB t0: 0.6852
-  LGBM t0: 0.6296
-  ET t0: 0.6852

.temp/log_xgboost.txt

@@ -1,15 +0,0 @@
-🚀 BẮT ĐẦU HUẤN LUYỆN MÔ HÌNH XGBOOST (GPU & CACHE)
-Initializing FeatureExtractor (mode=extended)...
-📦 Đang load cache: training_data_507bd2ba4ec0d3fe107839cbf73a7a7d.joblib...
-✅ Loaded 632 samples từ cache!
-   ⚡ Đã bỏ qua download위성 data (tiết kiệm thời gian)
-[CACHE HIT] Using cached dataset with 632 samples
-Training XGBOOST model...
-Evaluating model...
-Generating classification report...
-Saving model...
-[MODEL MANAGER] Saving model to: model_train/model_xgboost_auto.joblib
-[MODEL MANAGER] Saving metadata to: model_train/model_xgboost_auto_info.json
-[MODEL MANAGER] Model saved successfully!
-Training complete!
-✅ Hoàn thành! Accuracy: 0.6378

.temp/patch_caching.py

@@ -1,231 +0,0 @@
-import re
-import os
-
-with open('/home/x79/remote-sensing/new_import_ODC.py', 'r', encoding='utf-8') as f:
-    content = f.read()
-
-# 1. load_data
-load_data_replacement = """def load_data(dc, date_range, longtitude_range, latitude_range):
-    import os, hashlib
-    cache_dir = "dataset_cache"
-    os.makedirs(cache_dir, exist_ok=True)
-    key_str = f"s2_{date_range}_{longtitude_range}_{latitude_range}"
-    cache_key = hashlib.md5(key_str.encode()).hexdigest() + ".nc"
-    cache_path = os.path.join(cache_dir, cache_key)
-    
-    if os.path.exists(cache_path):
-        print(f"✅ Loading cached S2 data from {cache_path}")
-        return xr.open_dataset(cache_path)
-
-    product = 's2_l2a'
-    bbox = [longtitude_range[0], latitude_range[0], longtitude_range[1], latitude_range[1]]
-    
-    import pystac_client
-    import planetary_computer
-    import odc.stac
-    
-    catalog = pystac_client.Client.open(
-        "https://planetarycomputer.microsoft.com/api/stac/v1",
-        modifier=planetary_computer.sign_inplace,
-    )
-    search = catalog.search(
-        collections=["sentinel-2-l2a"],
-        bbox=bbox,
-        datetime=f"{date_range[0]}/{date_range[1]}",
-    )
-    items = list(search.items())
-    
-    data = odc.stac.load(
-        items,
-        bands=["red", "nir", "SCL"],
-        bbox=bbox,
-        crs="EPSG:32648",
-        resolution=RESOLUTION,
-        chunks={"x": 2048, "y": 2048, "time": 1},
-        groupby="solar_day"
-    )
-    if "SCL" in data.data_vars:
-        data = data.rename({"SCL": "scl"})
-        
-    print(f"💾 Caching S2 data to {cache_path}")
-    data = data.compute()
-    data.to_netcdf(cache_path)
-    
-    return data"""
-content = re.sub(r'def load_data\(dc, date_range, longtitude_range, latitude_range\):.*?return data', load_data_replacement, content, flags=re.DOTALL)
-
-
-# 2. load_sen1
-load_sen1_replacement = """def load_sen1(bbox, time_range):
-    import os, hashlib
-    cache_dir = "dataset_cache"
-    os.makedirs(cache_dir, exist_ok=True)
-    key_str = f"s1_vh_vv_{bbox}_{time_range}"
-    cache_key_vh = hashlib.md5((key_str + "vh").encode()).hexdigest() + ".nc"
-    cache_key_vv = hashlib.md5((key_str + "vv").encode()).hexdigest() + ".nc"
-    cache_path_vh = os.path.join(cache_dir, cache_key_vh)
-    cache_path_vv = os.path.join(cache_dir, cache_key_vv)
-    
-    if os.path.exists(cache_path_vh) and os.path.exists(cache_path_vv):
-        print(f"✅ Loading cached S1 data from {cache_path_vh} and {cache_path_vv}")
-        return xr.open_dataarray(cache_path_vh), xr.open_dataarray(cache_path_vv)
-
-    import pystac_client
-    import planetary_computer
-    import odc.stac
-    
-    # Kết nối STAC Client
-    catalog = pystac_client.Client.open(
-        "https://planetarycomputer.microsoft.com/api/stac/v1",
-        modifier=planetary_computer.sign_inplace,
-    )
-
-    # Tìm kiếm Items
-    search = catalog.search(
-        collections=["sentinel-1-rtc"],
-        bbox=bbox,
-        datetime=time_range,
-    )
-    items = list(search.items())
-
-    # Tải dữ liệu thành xarray Dataset
-    ds_s1 = odc.stac.load(
-        items,
-        bands=["vv", "vh"],
-        bbox=bbox,
-        crs="EPSG:32648",
-        resolution=RESOLUTION,
-        chunks={"x": 2048, "y": 2048, "time": 1}
-    )
-
-    # Tính giá trị trung vị theo thời gian
-    ds_median = ds_s1.median(dim="time").compute()
-    vv = ds_median["vv"]
-    vh = ds_median["vh"]
-
-    # Thêm chiều 'band' để giống hệt rioxarray
-    vv = vv.expand_dims(dim="band")
-    vh = vh.expand_dims(dim="band")
-
-    # Phục hồi metadata về toạ độ
-    vv = vv.rio.write_crs("EPSG:32648")
-    vh = vh.rio.write_crs("EPSG:32648")
-
-    print(f"💾 Caching S1 data to {cache_dir}")
-    vh.to_netcdf(cache_path_vh)
-    vv.to_netcdf(cache_path_vv)
-
-    return vh, vv"""
-content = re.sub(r'def load_sen1\(bbox, time_range\):.*?return vh, vv', load_sen1_replacement, content, flags=re.DOTALL)
-
-
-# 3. load_data_sen1
-load_data_sen1_replacement = """def load_data_sen1(dc, date_range, coordinates):
-    import os, hashlib
-    longtitude_range, latitude_range = coordinates
-    bbox = [longtitude_range[0], latitude_range[0], longtitude_range[1], latitude_range[1]]
-    
-    cache_dir = "dataset_cache"
-    os.makedirs(cache_dir, exist_ok=True)
-    key_str = f"data_sen1_{date_range}_{bbox}"
-    cache_key_vh = hashlib.md5((key_str + "vh").encode()).hexdigest() + ".nc"
-    cache_key_vv = hashlib.md5((key_str + "vv").encode()).hexdigest() + ".nc"
-    cache_path_vh = os.path.join(cache_dir, cache_key_vh)
-    cache_path_vv = os.path.join(cache_dir, cache_key_vv)
-    
-    if os.path.exists(cache_path_vh) and os.path.exists(cache_path_vv):
-        print(f"✅ Loading cached S1 (coord) data")
-        return xr.open_dataarray(cache_path_vh), xr.open_dataarray(cache_path_vv)
-    
-    import pystac_client
-    import planetary_computer
-    import odc.stac
-    
-    catalog = pystac_client.Client.open(
-        "https://planetarycomputer.microsoft.com/api/stac/v1",
-        modifier=planetary_computer.sign_inplace,
-    )
-    search = catalog.search(
-        collections=["sentinel-1-rtc"],
-        bbox=bbox,
-        datetime=f"{date_range[0]}/{date_range[1]}",
-    )
-    items = list(search.items())
-    
-    data_sen1 = odc.stac.load(
-        items,
-        bands=["vv", "vh"],
-        bbox=bbox,
-        crs="EPSG:32648",
-        resolution=RESOLUTION,
-        chunks={"x": 2048, "y": 2048, "time": 1},
-        groupby="solar_day"
-    )
-    
-    data_sen1 = data_sen1.compute()
-    dsvh = data_sen1.vh
-    dsvv = data_sen1.vv
-    
-    print(f"💾 Caching S1 (coord) data")
-    dsvh.to_netcdf(cache_path_vh)
-    dsvv.to_netcdf(cache_path_vv)
-    
-    return dsvh, dsvv"""
-content = re.sub(r'def load_data_sen1\(dc, date_range, coordinates\):.*?return dsvh, dsvv', load_data_sen1_replacement, content, flags=re.DOTALL)
-
-
-# 4. load_data_sen2
-load_data_sen2_replacement = """def load_data_sen2(dc, date_range, coordinates):
-    import os, hashlib
-    longtitude_range, latitude_range = coordinates
-    bbox = [longtitude_range[0], latitude_range[0], longtitude_range[1], latitude_range[1]]
-    
-    cache_dir = "dataset_cache"
-    os.makedirs(cache_dir, exist_ok=True)
-    key_str = f"data_sen2_{date_range}_{bbox}"
-    cache_key = hashlib.md5(key_str.encode()).hexdigest() + ".nc"
-    cache_path = os.path.join(cache_dir, cache_key)
-    
-    if os.path.exists(cache_path):
-        print(f"✅ Loading cached S2 (coord) data from {cache_path}")
-        return xr.open_dataset(cache_path)
-    
-    import pystac_client
-    import planetary_computer
-    import odc.stac
-    
-    catalog = pystac_client.Client.open(
-        "https://planetarycomputer.microsoft.com/api/stac/v1",
-        modifier=planetary_computer.sign_inplace,
-    )
-    search = catalog.search(
-        collections=["sentinel-2-l2a"],
-        bbox=bbox,
-        datetime=f"{date_range[0]}/{date_range[1]}",
-    )
-    items = list(search.items())
-    
-    data = odc.stac.load(
-        items,
-        bands=["red", "nir", "SCL"],
-        bbox=bbox,
-        crs="EPSG:32648",
-        resolution=RESOLUTION,
-        chunks={"x": 2048, "y": 2048, "time": 1},
-        groupby="solar_day"
-    )
-    if "SCL" in data.data_vars:
-        data = data.rename({"SCL": "scl"})
-        
-    data = data.compute()
-    print(f"💾 Caching S2 (coord) data to {cache_path}")
-    data.to_netcdf(cache_path)
-    
-    return data"""
-content = re.sub(r'def load_data_sen2\(dc, date_range, coordinates\):.*?return data', load_data_sen2_replacement, content, flags=re.DOTALL)
-
-
-with open('/home/x79/remote-sensing/new_import_ODC.py', 'w', encoding='utf-8') as f:
-    f.write(content)
-
-print("Patching successful.")

.temp/patch_load_sen1.py

@@ -1,29 +0,0 @@
-import json
-import glob
-
-def fix_load_sen1(file_path):
-    with open(file_path, 'r', encoding='utf-8') as f:
-        nb = json.load(f)
-        
-    changed = False
-    for cell in nb.get('cells', []):
-        if cell.get('cell_type') == 'code':
-            source = cell.get('source', [])
-            for i, line in enumerate(source):
-                if 'load_sen1(name_vh, name_vv)' in line:
-                    indent = line[:len(line) - len(line.lstrip())]
-                    replacement = (
-                        f"{indent}bbox = [longtitude_range[0], latitude_range[0], longtitude_range[1], latitude_range[1]]\n"
-                        f"{indent}time_range = f'{{date_range[0]}}/{{date_range[1]}}'\n"
-                        f"{indent}{line.lstrip().replace('load_sen1(name_vh, name_vv)', 'load_sen1(bbox, time_range)')}"
-                    )
-                    source[i] = replacement
-                    changed = True
-                    
-    if changed:
-        with open(file_path, 'w', encoding='utf-8') as f:
-            json.dump(nb, f, indent=1)
-        print(f"Patched load_sen1 in {file_path}")
-
-for nb in glob.glob("*.ipynb"):
-    fix_load_sen1(nb)

.temp/patch_notebooks.py

@@ -1,43 +0,0 @@
-import json
-import glob
-
-def patch_notebook(file_path):
-    with open(file_path, 'r', encoding='utf-8') as f:
-        nb = json.load(f)
-        
-    changed = False
-    for cell in nb.get('cells', []):
-        if cell.get('cell_type') == 'code':
-            source = cell.get('source', [])
-            
-            # Check if this cell should be fully commented out
-            full_source = ''.join(source)
-            if 'dc.load(' in full_source or 'ds.vv' in full_source:
-                for i in range(len(source)):
-                    if not source[i].startswith('#'):
-                        source[i] = '# ' + source[i]
-                changed = True
-                continue
-            
-            # Otherwise, do line-by-line replacements
-            for i, line in enumerate(source):
-                if 'ST_training data_updated_1130points.shp' in line:
-                    source[i] = line.replace('ST_training data_updated_1130points.shp', 'ST_training_data_updated_1130points.shp')
-                    changed = True
-                if 'from new_import import *' in line:
-                    source[i] = line.replace('from new_import import *', 'from new_import_ODC import *')
-                    changed = True
-                if 'dc = datacube.Datacube()' in line:
-                    source[i] = line.replace('dc = datacube.Datacube()', 'dc = None')
-                    changed = True
-                if 'load_data(dc,' in line:
-                    source[i] = line.replace('load_data(dc,', 'load_data(None,')
-                    changed = True
-                    
-    if changed:
-        with open(file_path, 'w', encoding='utf-8') as f:
-            json.dump(nb, f, indent=1)
-        print(f"Patched {file_path}")
-
-for nb in glob.glob("*.ipynb"):
-    patch_notebook(nb)

.temp/patch_rf_to_xgb.py

@@ -1,75 +0,0 @@
-import json
-import glob
-import re
-
-def patch_python_script(filepath):
-    try:
-        with open(filepath, 'r', encoding='utf-8') as f:
-            content = f.read()
-            
-        original_content = content
-        
-        # Replace imports
-        content = re.sub(r'from sklearn\.ensemble import RandomForestClassifier', 
-                         'from xgboost import XGBClassifier', content)
-        
-        # Replace the model instantiations (for 01.train_ODC.py)
-        rf_pattern = re.compile(r'model\s*=\s*RandomForestClassifier\([^)]+\)', re.DOTALL)
-        xgb_replacement = """model = XGBClassifier(
-                    n_estimators=200,
-                    max_depth=30,
-                    tree_method="hist",
-                    device="cuda",
-                    random_state=42,
-                    n_jobs=-1,
-                    verbosity=1
-                )"""
-        
-        content = rf_pattern.sub(xgb_replacement, content)
-        
-        if content != original_content:
-            with open(filepath, 'w', encoding='utf-8') as f:
-                f.write(content)
-            print(f"Patched {filepath}")
-    except Exception as e:
-        print(f"Error patching {filepath}: {e}")
-
-def patch_notebook(filepath):
-    try:
-        with open(filepath, 'r', encoding='utf-8') as f:
-            nb = json.load(f)
-            
-        changed = False
-        import_pattern = re.compile(r'from\s+sklearn\.ensemble\s+import\s+RandomForestClassifier')
-        inst_pattern = re.compile(r'RandomForestClassifier\([^)]*\)')
-        
-        for cell in nb.get('cells', []):
-            if cell.get('cell_type') == 'code':
-                source = cell.get('source', [])
-                for i in range(len(source)):
-                    if import_pattern.search(source[i]):
-                        source[i] = import_pattern.sub('from xgboost import XGBClassifier', source[i])
-                        changed = True
-                        
-                    if inst_pattern.search(source[i]):
-                        source[i] = inst_pattern.sub("XGBClassifier(tree_method='hist', device='cuda', random_state=42, n_jobs=-1)", source[i])
-                        changed = True
-                        
-                    if "'classifier__criterion': ['gini', 'entropy']" in source[i]:
-                        source[i] = source[i].replace("'classifier__criterion': ['gini', 'entropy']",
-                                                      "'classifier__learning_rate': [0.01, 0.1, 0.2]")
-                        changed = True
-                        
-        if changed:
-            with open(filepath, 'w', encoding='utf-8') as f:
-                json.dump(nb, f, indent=1)
-            print(f"Patched {filepath}")
-    except Exception as e:
-        print(f"Error patching {filepath}: {e}")
-
-if __name__ == "__main__":
-    patch_python_script("01.train_ODC.py")
-    patch_python_script("new_train.py")
-    
-    for nb in glob.glob("*.ipynb"):
-        patch_notebook(nb)

.temp/processMask-test.py

@@ -1,272 +0,0 @@
-#!/usr/bin/env python
-# coding: utf-8
-
-# In[1]:
-
-
-get_ipython().run_cell_magic('time', '', '%matplotlib inline\nfrom new_import import *\n')
-
-
-# In[2]:
-
-
-get_ipython().run_cell_magic('time', '', '# Dask gateway\ncluster, client = notebook_utils.initialize_dask(use_gateway=True, workers=(1,4))\ndc = datacube.Datacube()\n\n# Configure s3 access\nconfigure_s3_access(aws_unsigned=False, requester_pays=True, client=client)\n\nclient\n')
-
-
-# In[3]:
-
-
-## cấu hình thời gian lấy ảnh và tọa độ
-# date_range = ('2022-09-01', '2023-10-01')
-# longtitude_range = (105.86575, 105.94120)
-# latitude_range = (9.65070, 9.69850)
-date_range = ('2022-09-01', '2023-10-01')
-longtitude_range = (105.5, 106.4)
-latitude_range = (9.2, 10.0) 
-
-
-# In[4]:
-
-
-## truy vấn ảnh vệ tinh sen2
-data = load_data(dc, date_range, longtitude_range, latitude_range)
-notebook_utils.heading(notebook_utils.xarray_object_size(data))
-display(data)
-
-
-# In[5]:
-
-
-# Specify the start and end times 
-min_date = '2022-09-01' # Thời gian bắt đầu lấy data cho quá trình train
-max_date = '2023-10-01' # Thời gian kết thúc lấy data cho quá trình train
-# Just do 1 month for testing
-# max_date = '2022-10-01' # Thời gian kết thúc lấy data cho quá trình train
-
-# Specify a spatail region to search using latitude/longitude cooridinates
-min_longitude, max_longitude = (105.5, 106.4)
-min_latitude, max_latitude = (9.2, 10.0)
-
-# Specify the product. In this case we want to use Sentinel-2 Level-2A data
-product = 's2_l2a'
-
-# Construct the search query dictionary
-query = {
-    'product': product,                     # Product name
-    'x': (min_longitude, max_longitude),    # "x" axis bounds
-    'y': (min_latitude, max_latitude),      # "y" axis bounds
-    'time': (min_date, max_date),           # Any parsable date strings
-}
-
-
-# In[6]:
-
-
-# Most common CRS
-native_crs = notebook_utils.mostcommon_crs(dc, query)
-print(f'Most common native CRS: {native_crs}')
-
-
-# In[7]:
-
-
-# Specify the spectral band measurements we want to use for a classification algorithm
-measurements = ['red', 'nir', 'scl']
-
-load_params = {
-    'measurements': measurements,                   # Selected measurement or alias names
-    'output_crs': native_crs,                       # Target EPSG code
-    'resolution': (-10, 10),                        # Target resolution
-    'group_by': 'solar_day',                        # Scene grouping
-    'dask_chunks': {'x': 2048, 'y': 2048},          # Dask chunks
-}
-
-
-# In[8]:
-
-
-get_ipython().run_cell_magic('time', '', '# The replacement "dc.load()" function for this product\ndata = load_s2l2a_with_offset(\n    dc,\n    query | load_params   # Combine the two dicts that contain our search and load parameters\n)\n\n# This line prints the total size of the dataset hat was loaded\nnotebook_utils.heading(notebook_utils.xarray_object_size(data))\n\ndisplay(data)\n')
-
-
-# In[9]:
-
-
-# %%time
-# # Tiến hành loại bỏ các vị trí bị mây ảnh hưởng
-# result = mask_clean(data)
-# progress(result)
-
-
-# In[10]:
-
-
-# Tiến hành tính toán NDVI
-ds1 = calculate_indices(result, index='NDVI', satellite_mission='s2')
-ndvi = ds1["NDVI"]
-display(ndvi)
-
-
-# In[11]:
-
-
-get_ipython().run_cell_magic('time', '', "## tính ndvi theo tháng\naverage_ndvi = ndvi.resample(time='1M').mean().persist()\nprogress(average_ndvi)\n")
-
-
-# In[12]:
-
-
-# compute average_ndvi
-average_ndvi = average_ndvi.compute()
-
-
-# In[13]:
-
-
-# cấu hình vh vv file
-# name_vh = "ThuanHoa/ThuanHoa_VH.tif"
-# name_vv = "ThuanHoa/ThuanHoa_VV.tif"
-
-# load dữ liệu sen1
-bbox = [105.5, 9.2, 106.4, 10.0]
-time_range = '2022-09-01/2023-10-01'
-# dsvh, dsvv = load_sen1(bbox, time_range)
-
-name_vh = "vh-0922_0923-full_ST.tif"
-name_vv = "vv-0922_0923-full_ST.tif"
-
-if not os.path.exists(name_vh):
-    get_ipython().system('aws s3 cp s3://easi-asia-dc-data/staging/ctu/sentinel-1/vh-0922_0923-full_ST.tif vh-0922_0923-full_ST.tif')
-if not os.path.exists(name_vv):
-    get_ipython().system('aws s3 cp s3://easi-asia-dc-data/staging/ctu/sentinel-1/vv-0922_0923-full_ST.tif vv-0922_0923-full_ST.tif')
-    
-bbox = [105.5, 9.2, 106.4, 10.0]
-time_range = '2022-09-01/2023-10-01'
-dsvh, dsvv = load_sen1(bbox, time_range)
-
-
-# In[27]:
-
-
-from sklearn.preprocessing import PolynomialFeatures
-from sklearn.linear_model import LinearRegression
-from sklearn.ensemble import RandomForestRegressor
-
-
-# In[28]:
-
-
-average_ndvi = average_ndvi[:, :7680, :8687]
-mask = ~np.isnan(average_ndvi)
-print(average_ndvi.shape)
-print(dsvh.shape)
-print(dsvv.shape)
-print(mask.shape)
-X_train = np.stack([dsvh.values[mask], dsvv.values[mask]], axis=1)
-y_train = average_ndvi.values[mask]
-
-
-# In[29]:
-
-
-model = LinearRegression()
-model.fit(X_train, y_train)
-
-
-# In[30]:
-
-
-X_pred = np.stack([dsvh.values[~mask], dsvv.values[~mask]], axis=1)
-average_ndvi.values[~mask] = model.predict(X_pred)
-
-
-# In[31]:
-
-
-average_ndvi_filled = xr.DataArray(average_ndvi, dims=average_ndvi.dims)
-
-
-# In[32]:
-
-
-plt.imshow(average_ndvi_filled.isel(time=6))
-
-
-# In[65]:
-
-
-plt.imshow(average_ndvi.isel(time=6))
-
-
-# In[33]:
-
-
-train_path = "train/ST_training data_updated_1130points.shp"
-
-
-# In[34]:
-
-
-train = load_train_data(train_path)
-
-
-# In[37]:
-
-
-datasets = get_data_sen1_and_sen2(train, average_ndvi_filled, dsvh, dsvv)
-
-
-# In[39]:
-
-
-# cấu hình nhãn dữ liệu
-label_mapping = {
-    "Lua tom": "0",
-    "Lua": "1",
-    "CHN": "2",
-    "CLN": "3",
-    "TS": "4",
-    "Song": "5",
-    "Dat xay dung": "6",
-    "Rung": "7"
-}
-
-# chia tập dữ liệu train, val, test
-X_train, X_val, X_test, y_train, y_val, y_test = split_train_data(train, label_mapping, datasets)
-
-
-# In[40]:
-
-
-# Huấn luyện mô hình
-grid_search = train_with_rf(X_train, X_val, y_train, y_val)
-
-
-# In[41]:
-
-
-# kiểm tra độ chính xác với tập test
-y_pred_test = grid_search.predict(X_test)
-test_accuracy = accuracy_score(y_test, y_pred_test)
-print(f"Accuracy for test data {round(test_accuracy, 2)*100} %")
-
-
-# In[42]:
-
-
-# Lưu mô hình huấn luyện
-save_model("model_new.joblib", grid_search)
-
-
-# In[43]:
-
-
-# đóng client, cluster
-client.close()
-cluster.close()
-
-
-# In[ ]:
-
-
-
-

.temp/processMask.py

@@ -1,127 +0,0 @@
-#!/usr/bin/env python
-# coding: utf-8
-
-# In[1]:
-
-
-get_ipython().run_cell_magic('time', '', '%matplotlib inline\nfrom new_import import *\n')
-
-
-# In[2]:
-
-
-get_ipython().run_cell_magic('time', '', '# Dask gateway\ncluster, client = notebook_utils.initialize_dask(use_gateway=True, workers=(1,4))\ndc = datacube.Datacube()\n\n# Configure s3 access\nconfigure_s3_access(aws_unsigned=False, requester_pays=True, client=client)\n\nclient\n')
-
-
-# In[3]:
-
-
-## cấu hình thời gian lấy ảnh và tọa độ
-date_range = ('2022-09-01', '2023-10-01')
-longtitude_range = (105.86575, 105.94120)
-latitude_range = (9.65070, 9.69850)
-
-
-# In[4]:
-
-
-## truy vấn ảnh vệ tinh sen2
-data = load_data(dc, date_range, longtitude_range, latitude_range)
-notebook_utils.heading(notebook_utils.xarray_object_size(data))
-display(data)
-
-
-# In[5]:
-
-
-get_ipython().run_cell_magic('time', '', '# Tiến hành loại bỏ các vị trí bị mây ảnh hưởng\nresult = mask_clean(data)\nprogress(result)\n')
-
-
-# In[6]:
-
-
-# Tiến hành tính toán NDVI
-ds1 = calculate_indices(result, index='NDVI', satellite_mission='s2')
-ndvi = ds1["NDVI"]
-display(ndvi)
-
-
-# In[17]:
-
-
-get_ipython().run_cell_magic('time', '', "## tính ndvi theo tháng\naverage_ndvi = ndvi.resample(time='1M').mean().persist()\nprogress(average_ndvi)\n")
-
-
-# In[18]:
-
-
-# compute average_ndvi
-average_ndvi = average_ndvi.compute()
-
-
-# In[9]:
-
-
-# cấu hình vh vv file
-name_vh = "ThuanHoa/ThuanHoa_VH.tif"
-name_vv = "ThuanHoa/ThuanHoa_VV.tif"
-
-# load dữ liệu sen1
-bbox = [105.5, 9.2, 106.4, 10.0]
-time_range = '2022-09-01/2023-10-01'
-dsvh, dsvv = load_sen1(bbox, time_range)
-
-
-# In[10]:
-
-
-from sklearn.preprocessing import PolynomialFeatures
-from sklearn.linear_model import LinearRegression
-from sklearn.ensemble import RandomForestRegressor
-
-
-# In[11]:
-
-
-mask = ~np.isnan(average_ndvi)
-X_train = np.stack([dsvh.values[mask], dsvv.values[mask]], axis=1)
-y_train = average_ndvi.values[mask]
-
-
-# In[12]:
-
-
-model = LinearRegression()
-model.fit(X_train, y_train)
-
-
-# In[13]:
-
-
-X_pred = np.stack([dsvh.values[~mask], dsvv.values[~mask]], axis=1)
-average_ndvi.values[~mask] = model.predict(X_pred)
-
-
-# In[14]:
-
-
-average_ndvi_filled = xr.DataArray(average_ndvi, dims=average_ndvi.dims)
-
-
-# In[16]:
-
-
-plt.imshow(average_ndvi_filled.isel(time=1))
-
-
-# In[19]:
-
-
-plt.imshow(average_ndvi.isel(time=1))
-
-
-# In[ ]:
-
-
-
-

.temp/rebuild_notebook.py

@@ -1,145 +0,0 @@
-import json
-
-def get_content(filename):
-    with open(filename, "r", encoding="utf-8") as f:
-        return f.read()
-
-fe_content = get_content("feature_extractor.py")
-tm_content = get_content("train_module.py")
-dt_content = get_content("train_land_decision_tree_gpu.py")
-
-notebook = {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tải Dữ liệu Vệ tinh qua Colab (Self-contained)\n",
-    "Notebook này đã được nhúng sẵn toàn bộ mã nguồn xử lý. Bạn không cần upload cả thư mục `remote-sensing` nữa.\n",
-    "\n",
-    "## Bước 1: Upload Shapefile (BẮT BUỘC)\n",
-    "Mô hình cần biết các điểm tọa độ đất để lấy dữ liệu. Hãy nén thư mục `train/` trên máy bạn thành `train.zip` và chạy ô dưới đây để upload nó trực tiếp lên Colab."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": None,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from google.colab import files\n",
-    "import os\n",
-    "\n",
-    "print(\"Hãy chọn file train.zip từ máy tính của bạn:\")\n",
-    "uploaded = files.upload()\n",
-    "\n",
-    "if \"train.zip\" in uploaded:\n",
-    "    !unzip -q -o train.zip -d /content/train_tmp/\n",
-    "    # Move the extracted files directly to /content/train/\n",
-    "    !mkdir -p /content/train\n",
-    "    !mv /content/train_tmp/*/* /content/train/ 2>/dev/null || mv /content/train_tmp/* /content/train/\n",
-    "    print(\"Đã giải nén shapefile thành công vào thư mục /content/train/\")\n",
-    "else:\n",
-    "    print(\"LỖI: Bạn chưa upload file có tên là train.zip!\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Bước 2: Cài đặt thư viện & Tạo môi trường"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": None,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install planetary-computer pystac-client odc-stac geopandas rasterio xarray joblib scikit-learn xgboost lightgbm"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": None,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%writefile feature_extractor.py\n" + fe_content
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": None,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%writefile train_module.py\n" + tm_content
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": None,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%writefile train_land_decision_tree_gpu.py\n" + dt_content
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Bước 3: Chạy tiến trình tải ảnh vệ tinh và tạo Cache"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": None,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!python train_land_decision_tree_gpu.py"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Bước 4: Tải file Cache về máy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": None,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from google.colab import files\n",
-    "import glob\n",
-    "\n",
-    "cache_files = glob.glob(\"dataset_cache/*.joblib\")\n",
-    "if cache_files:\n",
-    "    latest_cache = max(cache_files, key=os.path.getctime)\n",
-    "    print(f\"Đang tải file {latest_cache} về máy...\")\n",
-    "    files.download(latest_cache)\n",
-    "else:\n",
-    "    print(\"Chưa tìm thấy file cache. Hãy chắc chắn bước 3 đã chạy thành công!\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
-
-with open("Download_Cache_Colab.ipynb", "w", encoding="utf-8") as f:
-    json.dump(notebook, f, indent=1, ensure_ascii=False)
-
-print("Notebook updated successfully!")

.temp/remove_datacube.py

@@ -1,35 +0,0 @@
-import json
-import glob
-
-def fix_notebook(file_path):
-    with open(file_path, 'r', encoding='utf-8') as f:
-        nb = json.load(f)
-    changed = False
-    for cell in nb.get('cells', []):
-        if cell.get('cell_type') == 'code':
-            source = cell.get('source', [])
-            if isinstance(source, list):
-                for i, line in enumerate(source):
-                    if "dc = datacube.Datacube()" in line:
-                        source[i] = "dc = None\n"
-                        changed = True
-                    if "ds = dc.load(" in line:
-                        source[i] = "ds = None\n"
-                        changed = True
-                    if "data = dc.load(" in line:
-                        source[i] = "data = None\n"
-                        changed = True
-                    # If ds is None, ds.vv will fail
-                    if "vv_data = ds.vv" in line:
-                        source[i] = "vv_data = None\n"
-                        changed = True
-                    if "notebook_utils.xarray_object_size(ds)" in line:
-                        source[i] = line.replace("notebook_utils.xarray_object_size(ds)", "'ds is None'")
-                        changed = True
-    if changed:
-        with open(file_path, 'w', encoding='utf-8') as f:
-            json.dump(nb, f, indent=1)
-        print(f"Removed datacube from {file_path}")
-
-for nb in glob.glob("*.ipynb"):
-    fix_notebook(nb)

.temp/run_mock_parallel_and_compare.py

@@ -1,110 +0,0 @@
-import os
-import glob
-import json
-import subprocess
-import time
-from tabulate import tabulate
-
-scripts = [
-    "train_land_randomforest.py",
-    "train_cloud_cnn.py",
-    "train_cloud_swin_unet.py",
-    "train_ndvi_statistical.py",
-    "train_ndvi_lstm_gru.py",
-    "train_ndvi_convlstm.py",
-    "train_ndvi_hybrid_physics.py",
-    "train_ndvi_ensemble.py"
-]
-
-print("🚀 Đang khởi chạy song song tất cả các mô hình...")
-processes = []
-for script in scripts:
-    if os.path.exists(script):
-        cmd = f"source /home/x79/miniconda3/etc/profile.d/conda.sh && conda activate env_01 && python {script}"
-        p = subprocess.Popen(["bash", "-c", cmd], stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
-        processes.append((script, p))
-
-for script, p in processes:
-    p.wait()
-
-print("✅ Đã chạy xong tất cả các mô hình!\n")
-print("📊 BẢNG SO SÁNH KẾT QUẢ CÁC MÔ HÌNH\n")
-
-# 1. Phân loại đất
-print("### 1. Nhóm Phân loại Lớp phủ (Land Classification)")
-land_data = []
-
-# Đọc XGBoost từ thư mục gốc
-if os.path.exists("model_xgboost_info.json"):
-    with open("model_xgboost_info.json", 'r') as f:
-        data = json.load(f)
-        params = data.get('params', {})
-        param_str = f"estimators:{params.get('n_estimators')}, depth:{params.get('max_depth')}" if params else "N/A"
-        land_data.append([
-            data.get('model_type', 'XGBoost'),
-            data.get('accuracy', ''),
-            data.get('precision', ''),
-            data.get('recall', ''),
-            data.get('f1_score', ''),
-            param_str
-        ])
-
-# Đọc các model khác trong model_train
-for info_file in glob.glob("model_train/*_info.json"):
-    with open(info_file, 'r') as f:
-        data = json.load(f)
-        # Chỉ lấy các model có độ chính xác (để lọc model rác/cũ)
-        if 'accuracy' not in data and 'f1_score' not in data:
-            continue
-            
-        params = data.get('params', {})
-        param_str = f"estimators:{params.get('n_estimators')}, depth:{params.get('max_depth')}" if params else "N/A"
-        
-        # Fallback for Random Forest
-        if data.get('model_type') == 'RandomForest_RealData':
-            param_str = "estimators:100, depth:15"
-            
-        land_data.append([
-            data.get('model_type', ''),
-            data.get('accuracy', ''),
-            data.get('precision', ''),
-            data.get('recall', ''),
-            data.get('f1_score', ''),
-            param_str
-        ])
-
-if land_data:
-    print(tabulate(land_data, headers=["Model", "Accuracy", "Precision", "Recall", "F1-Score", "Parameters"], tablefmt="github"))
-print("\n")
-
-# 2. Xóa mây
-print("### 2. Nhóm Xóa mây (Cloud Removal)")
-cloud_data = []
-for info_file in glob.glob("cloud_removal_model/*_info.json"):
-    with open(info_file, 'r') as f:
-        data = json.load(f)
-        cloud_data.append([
-            data.get('model_type', ''),
-            data.get('epoch', ''),
-            data.get('train_loss', ''),
-            data.get('val_loss', '')
-        ])
-if cloud_data:
-    print(tabulate(cloud_data, headers=["Model", "Epochs", "Train Loss", "Val Loss"], tablefmt="github"))
-print("\n")
-
-# 3. Dự báo NDVI
-print("### 3. Nhóm Dự báo Thực vật (NDVI Forecasting)")
-ndvi_data = []
-for info_file in glob.glob("ndvi_forecast_model/*_info.json"):
-    with open(info_file, 'r') as f:
-        data = json.load(f)
-        ndvi_data.append([
-            data.get('model_type', ''),
-            data.get('rmse', ''),
-            data.get('mae', ''),
-            data.get('epoch', 'N/A')
-        ])
-if ndvi_data:
-    print(tabulate(ndvi_data, headers=["Model", "RMSE", "MAE", "Epochs"], tablefmt="github"))
-print("\n")

.temp/test_VH_VV.py

@@ -1,72 +0,0 @@
-#!/usr/bin/env python
-# coding: utf-8
-
-# In[2]:
-
-
-get_ipython().run_line_magic('matplotlib', 'inline')
-from new_import import *
-
-
-# Dask gateway
-cluster, client = notebook_utils.initialize_dask(use_gateway=True, workers=(1,4))
-dc = datacube.Datacube()
-
-
-# Configure s3 access
-configure_s3_access(aws_unsigned=False, requester_pays=True, client=client)
-
-
-
-
-# In[3]:
-
-
-ds = dc.load(
-    product="sentinel1_grd_gamma0_20m",
-    x=(105.5, 106.4),
-    y=(9.2, 10.0),
-    time=("2022-09-01", "2023-10-01"),
-    measurements=["vv", "vh"],
-    output_crs="EPSG:32648",
-    resolution=(-10,10),
-    dask_chunks={"x":2048, "y":2048},
-    skip_broken_datasets=True,
-    group_by="solar_day"
-)
-notebook_utils.heading(notebook_utils.xarray_object_size(ds))
-ds
-
-
-# In[18]:
-
-
-vh = ds.vh.resample(time='1M').mean().persist()
-vh = vh.compute()
-vv = ds.vv.resample(time='1M').mean().persist()
-vv = vv.compute()
-
-
-
-# In[28]:
-
-
-vv.min()
-
-
-# In[33]:
-
-
-import matplotlib.pyplot as plt
-
-# Plot the data
-plt.imshow(vh.isel(time=0), cmap='viridis', vmin=0, vmax=1)
-plt.colorbar()  # Add colorbar for reference
-plt.show()
-
-
-# In[ ]:
-
-
-
-

.temp/test_bug.py

@@ -1,33 +0,0 @@
-import torch
-import numpy as np
-
-device = "cpu"
-input_array = np.zeros((4, 16, 16), dtype=np.float32)
-input_tensor = torch.from_numpy(input_array).unsqueeze(0).to(device)
-
-print("Before pad:", input_tensor.shape)
-
-from train_cloud_removal import UNet
-model = UNet(in_channels=6, out_channels=4).to(device)
-
-if hasattr(model, 'inc') and hasattr(model.inc.double_conv[0], 'in_channels'):
-    expected_channels = model.inc.double_conv[0].in_channels
-elif hasattr(model, 'conv1') and hasattr(model.conv1, 'in_channels'):
-    expected_channels = model.conv1.in_channels
-else:
-    expected_channels = list(model.parameters())[0].shape[1]
-
-print("Expected channels:", expected_channels)
-
-if expected_channels > input_tensor.shape[1]:
-    pad_channels = expected_channels - input_tensor.shape[1]
-    padding = torch.zeros(1, pad_channels, *input_tensor.shape[2:]).to(device)
-    input_tensor = torch.cat([input_tensor, padding], dim=1)
-
-print("After pad:", input_tensor.shape)
-
-try:
-    model(input_tensor)
-    print("Success!")
-except Exception as e:
-    print("Error:", e)

.temp/test_caching.py

@@ -1,25 +0,0 @@
-import os
-import sys
-sys.path.insert(0, os.getcwd())
-import new_import_ODC
-import time
-import xarray as xr
-
-# Mock minimal params to test load_data
-date_range = ('2023-01-01', '2023-01-31')
-longtitude_range = (105.0, 105.1)
-latitude_range = (9.5, 9.6)
-
-print("--- First Call (Downloading & Caching) ---")
-start = time.time()
-data1 = new_import_ODC.load_data(None, date_range, longtitude_range, latitude_range)
-end = time.time()
-print(f"Time taken: {end - start:.2f}s")
-
-print("--- Second Call (Loading from Cache) ---")
-start = time.time()
-data2 = new_import_ODC.load_data(None, date_range, longtitude_range, latitude_range)
-end = time.time()
-print(f"Time taken: {end - start:.2f}s")
-
-print("✅ Test completed")

.temp/test_checkpoint.py

@@ -1,7 +0,0 @@
-import torch
-checkpoint = torch.load('cloud_removal_model/cloud_removal_unet_best.pth', map_location='cpu')
-print(checkpoint.keys())
-print("in_channels in checkpoint:", 'in_channels' in checkpoint)
-if 'in_channels' in checkpoint:
-    print(checkpoint['in_channels'])
-print("Shape of inc.double_conv.0.weight:", checkpoint['model_state_dict']['inc.double_conv.0.weight'].shape)

.temp/test_cloud.py

@@ -1,4 +0,0 @@
-import torch
-from cloud_removal import DeepInpaintingStrategy
-cloud_remover = DeepInpaintingStrategy()
-print("Model channels:", list(cloud_remover.model.parameters())[0].shape[1])

.temp/test_cloud2.py

@@ -1,10 +0,0 @@
-from cloud_removal import DeepInpaintingStrategy
-import torch
-import numpy as np
-
-cr = DeepInpaintingStrategy(model_path="cloud_removal_model/cloud_removal_unet_best.pth")
-if cr.model is not None:
-    expected = list(cr.model.parameters())[0].shape[1]
-    print("Expected channels:", expected)
-else:
-    print("Failed to load model")

.temp/test_cloud_removal.py

@@ -1,194 +0,0 @@
-"""
-Test script for cloud_removal module
-Kiểm tra các phương pháp xử lý mây
-"""
-
-import numpy as np
-import xarray as xr
-from cloud_removal import (
-    process_cloud_removal,
-    get_available_methods,
-    compare_methods
-)
-
-
-def create_mock_s2_data():
-    """Tạo mock Sentinel-2 data để test"""
-    # Create synthetic data: 5 time steps, 100x100 pixels
-    np.random.seed(42)
-    
-    time_steps = 5
-    y_size = 100
-    x_size = 100
-    
-    # Create bands
-    bands = {}
-    for band in ["B02", "B03", "B04", "B08", "B11"]:
-        # Random reflectance values
-        data = np.random.rand(time_steps, y_size, x_size) * 0.3 + 0.1
-        bands[band] = (["time", "y", "x"], data)
-    
-    # Create SCL (Scene Classification Layer)
-    # Mostly vegetation (4), with some clouds
-    scl_data = np.full((time_steps, y_size, x_size), 4, dtype=np.uint8)
-    
-    # Add clouds (class 9) in random locations
-    for t in range(time_steps):
-        # Random cloud patches
-        n_clouds = np.random.randint(5, 15)
-        for _ in range(n_clouds):
-            y_start = np.random.randint(0, y_size - 20)
-            x_start = np.random.randint(0, x_size - 20)
-            cloud_height = np.random.randint(10, 20)
-            cloud_width = np.random.randint(10, 20)
-            scl_data[t, y_start:y_start+cloud_height, x_start:x_start+cloud_width] = 9
-    
-    bands["SCL"] = (["time", "y", "x"], scl_data)
-    
-    # Create xarray Dataset
-    ds = xr.Dataset(
-        bands,
-        coords={
-            "time": np.arange(time_steps),
-            "y": np.arange(y_size),
-            "x": np.arange(x_size)
-        }
-    )
-    
-    return ds
-
-
-def test_available_methods():
-    """Test lấy danh sách methods"""
-    print("=" * 60)
-    print("TEST: Get Available Methods")
-    print("=" * 60)
-    
-    methods = get_available_methods()
-    print(f"\nFound {len(methods)} methods:")
-    for method, description in methods.items():
-        print(f"  - {method:20s}: {description}")
-    
-    print("\n✅ Test passed!")
-
-
-def test_single_method(method_name="classic"):
-    """Test một method cụ thể"""
-    print("\n" + "=" * 60)
-    print(f"TEST: Cloud Removal Method '{method_name}'")
-    print("=" * 60)
-    
-    # Create mock data
-    s2_data = create_mock_s2_data()
-    print(f"\nMock data created: {dict(s2_data.dims)}")
-    
-    # Process clouds
-    cleaned_data, metadata = process_cloud_removal(
-        s2_data=s2_data,
-        method=method_name,
-        verbose=True
-    )
-    
-    # Check results
-    print(f"\nMetadata:")
-    print(f"  - Method: {metadata['method']}")
-    print(f"  - Cloud coverage: {metadata['cloud_coverage_percent']:.1f}%")
-    print(f"  - Masked pixels: {metadata['masked_pixels']:,}/{metadata['total_pixels']:,}")
-    print(f"  - Steps applied: {', '.join(metadata['steps_applied'])}")
-    
-    # Verify no NaN remaining
-    nan_count = 0
-    for band in cleaned_data.data_vars:
-        if band != "SCL":
-            nan_count += np.isnan(cleaned_data[band].values).sum()
-    
-    print(f"\nRemaining NaN pixels: {nan_count}")
-    
-    if nan_count == 0:
-        print("✅ Test passed - no NaN remaining!")
-    else:
-        print(f"⚠️  Warning - {nan_count} NaN pixels remaining")
-
-
-def test_comparison():
-    """Test so sánh nhiều methods"""
-    print("\n" + "=" * 60)
-    print("TEST: Compare Multiple Methods")
-    print("=" * 60)
-    
-    # Create mock data
-    s2_data = create_mock_s2_data()
-    
-    # Compare methods
-    methods_to_test = ["classic", "temporal_only", "median_composite", "ml_knn"]
-    
-    print(f"\nComparing {len(methods_to_test)} methods...")
-    results = compare_methods(s2_data, methods=methods_to_test)
-    
-    # Print summary
-    print("\n" + "-" * 60)
-    print(f"{'Method':<20} {'Success':<10} {'NaN %':<10} {'Steps'}")
-    print("-" * 60)
-    
-    for method, result in results.items():
-        if result['success']:
-            nan_pct = result['remaining_nan_percent']
-            steps = ', '.join(result['metadata']['steps_applied'][:2])  # First 2 steps
-            print(f"{method:<20} {'✅':<10} {nan_pct:>6.2f}%   {steps}")
-        else:
-            print(f"{method:<20} {'❌':<10} {'ERROR':<10} {result['error']}")
-    
-    print("-" * 60)
-    print("\n✅ Comparison test completed!")
-
-
-def test_edge_cases():
-    """Test các trường hợp đặc biệt"""
-    print("\n" + "=" * 60)
-    print("TEST: Edge Cases")
-    print("=" * 60)
-    
-    # Case 1: No SCL band
-    print("\n1. Testing without SCL band...")
-    s2_data = create_mock_s2_data()
-    s2_data_no_scl = s2_data.drop_vars("SCL")
-    
-    cleaned, meta = process_cloud_removal(s2_data_no_scl, method="classic", verbose=False)
-    print(f"   Result: {meta.get('warning', 'OK')}")
-    
-    # Case 2: 100% cloud coverage
-    print("\n2. Testing with 100% cloud coverage...")
-    s2_data_full_cloud = create_mock_s2_data()
-    s2_data_full_cloud["SCL"][:] = 9  # All clouds
-    
-    cleaned, meta = process_cloud_removal(s2_data_full_cloud, method="classic", verbose=False)
-    print(f"   Cloud coverage: {meta['cloud_coverage_percent']:.1f}%")
-    
-    # Case 3: No clouds
-    print("\n3. Testing with no clouds...")
-    s2_data_clear = create_mock_s2_data()
-    s2_data_clear["SCL"][:] = 4  # All vegetation
-    
-    cleaned, meta = process_cloud_removal(s2_data_clear, method="classic", verbose=False)
-    print(f"   Cloud coverage: {meta['cloud_coverage_percent']:.1f}%")
-    
-    print("\n✅ Edge case tests passed!")
-
-
-if __name__ == "__main__":
-    print("\n" + "🌥️  CLOUD REMOVAL MODULE TESTS 🌥️ ".center(60, "="))
-    print()
-    
-    # Run tests
-    test_available_methods()
-    test_single_method("classic")
-    test_single_method("hybrid")
-    test_comparison()
-    test_edge_cases()
-    
-    print("\n" + "=" * 60)
-    print("ALL TESTS COMPLETED!")
-    print("=" * 60)
-    print("\nModule is ready to use. Available methods:")
-    for method, desc in get_available_methods().items():
-        print(f"  • {method}")

.temp/test_cloud_training.py

@@ -1,28 +0,0 @@
-"""
-Script test nhanh cho cloud removal training
-"""
-
-import sys
-from pathlib import Path
-
-# Add winter_dataset to path
-sys.path.insert(0, str(Path(__file__).parent / "winter_dataset"))
-
-from train_cloud_removal import train_cloud_removal_model
-
-if __name__ == "__main__":
-    print("\n🌥️  Starting Cloud Removal Training Test")
-    print("=" * 70)
-    
-    # Test with small dataset
-    model, train_losses, val_losses = train_cloud_removal_model(
-        data_dir="winter_dataset",
-        use_s1=True,  # Use S1 radar data
-        batch_size=4,  # Small batch for testing
-        num_epochs=5,  # Few epochs for quick test
-        learning_rate=1e-4
-    )
-    
-    print("\n✅ Training test completed!")
-    print(f"Final train loss: {train_losses[-1]:.6f}")
-    print(f"Final val loss: {val_losses[-1]:.6f}")

.temp/test_cloud_upload.py

@@ -1,165 +0,0 @@
-#!/usr/bin/env python3
-"""
-Test Cloud Removal Model Upload Feature
-"""
-
-import requests
-import json
-from pathlib import Path
-
-# API base URL
-BASE_URL = "http://localhost:8000"
-
-def test_upload_cloud_model(file_path):
-    """Test uploading a cloud removal model"""
-    print(f"\n{'='*60}")
-    print("TEST 1: Upload Cloud Removal Model")
-    print(f"{'='*60}")
-    
-    if not Path(file_path).exists():
-        print(f"❌ File not found: {file_path}")
-        print("   Create a dummy .pth file for testing:")
-        print(f"   touch {file_path}")
-        return None
-    
-    with open(file_path, 'rb') as f:
-        files = {'file': (Path(file_path).name, f, 'application/octet-stream')}
-        
-        print(f"📤 Uploading: {file_path}")
-        response = requests.post(f"{BASE_URL}/api/cloud-removal/upload", files=files)
-        
-        if response.status_code == 200:
-            result = response.json()
-            print(f"✅ Upload successful!")
-            print(f"   Filename: {result['filename']}")
-            print(f"   Size: {result['size_mb']} MB")
-            print(f"   Path: {result['path']}")
-            return result['filename']
-        else:
-            print(f"❌ Upload failed: {response.status_code}")
-            print(f"   {response.json().get('detail', 'Unknown error')}")
-            return None
-
-def test_list_cloud_models():
-    """Test listing cloud removal models"""
-    print(f"\n{'='*60}")
-    print("TEST 2: List Cloud Removal Models")
-    print(f"{'='*60}")
-    
-    response = requests.get(f"{BASE_URL}/api/cloud-removal/models")
-    
-    if response.status_code == 200:
-        data = response.json()
-        print(f"✅ Found {data['count']} models:")
-        for i, model in enumerate(data['models'], 1):
-            print(f"\n   {i}. {model['filename']}")
-            print(f"      Size: {model['size_mb']} MB")
-            print(f"      Created: {model['created']}")
-            if 'epoch' in model:
-                print(f"      Epoch: {model['epoch']}, Val Loss: {model['val_loss']:.4f}")
-        return data['models']
-    else:
-        print(f"❌ Failed to list models: {response.status_code}")
-        return []
-
-def test_prediction_with_cloud_model(model_filename, cloud_model_filename):
-    """Test prediction using uploaded cloud removal model"""
-    print(f"\n{'='*60}")
-    print("TEST 3: Prediction with Custom Cloud Removal Model")
-    print(f"{'='*60}")
-    
-    config = {
-        "model_filename": model_filename,
-        "min_lon": 105.80,
-        "min_lat": 10.00,
-        "max_lon": 105.82,
-        "max_lat": 10.02,
-        "start_date": "2024-01-15",
-        "end_date": "2024-01-17",
-        "max_scenes": 2,
-        "cloud_cover": 30,
-        "resolution": 20,
-        "use_gpu": False,
-        "export_ndvi": True,
-        "export_classification": True,
-        "cloud_removal_method": "deep",
-        "cloud_removal_model": cloud_model_filename
-    }
-    
-    print("📊 Prediction Config:")
-    print(json.dumps(config, indent=2))
-    
-    print(f"\n🚀 Starting prediction with cloud removal model: {cloud_model_filename}")
-    response = requests.post(
-        f"{BASE_URL}/api/predict/with-ndvi",
-        json=config,
-        headers={'Content-Type': 'application/json'}
-    )
-    
-    if response.status_code == 200:
-        result = response.json()
-        print(f"✅ Prediction started!")
-        print(f"   Message: {result.get('message')}")
-        return result
-    else:
-        print(f"❌ Prediction failed: {response.status_code}")
-        print(f"   {response.json().get('detail', 'Unknown error')}")
-        return None
-
-def test_delete_cloud_model(filename):
-    """Test deleting a cloud removal model"""
-    print(f"\n{'='*60}")
-    print("TEST 4: Delete Cloud Removal Model")
-    print(f"{'='*60}")
-    
-    print(f"🗑️  Deleting: {filename}")
-    response = requests.delete(f"{BASE_URL}/api/cloud-removal/models/{filename}")
-    
-    if response.status_code == 200:
-        result = response.json()
-        print(f"✅ {result['message']}")
-        return True
-    else:
-        print(f"❌ Delete failed: {response.status_code}")
-        return False
-
-def main():
-    print("="*60)
-    print("CLOUD REMOVAL MODEL UPLOAD - FEATURE TEST")
-    print("="*60)
-    
-    # Test file path (create a dummy file for testing)
-    test_file = "test_cloud_removal_model.pth"
-    
-    # Create dummy file if it doesn't exist
-    if not Path(test_file).exists():
-        print(f"\n📝 Creating dummy test file: {test_file}")
-        Path(test_file).write_bytes(b"dummy_pytorch_model_data")
-    
-    # Run tests
-    uploaded_filename = test_upload_cloud_model(test_file)
-    
-    if uploaded_filename:
-        models = test_list_cloud_models()
-        
-        # Test prediction (requires a real land classification model)
-        print(f"\n{'='*60}")
-        print("NOTE: Prediction test requires a trained land classification model")
-        print("      Skipping prediction test in this demo")
-        print(f"{'='*60}")
-        
-        # Cleanup - delete test model
-        if input("\nDelete test model? (y/n): ").lower() == 'y':
-            test_delete_cloud_model(uploaded_filename)
-    
-    # Cleanup dummy file
-    if Path(test_file).exists():
-        Path(test_file).unlink()
-        print(f"\n🗑️  Cleaned up dummy file: {test_file}")
-    
-    print(f"\n{'='*60}")
-    print("TESTS COMPLETED")
-    print(f"{'='*60}")
-
-if __name__ == "__main__":
-    main()

.temp/test_empty_points.py

@@ -1,53 +0,0 @@
-import geopandas as gpd
-import planetary_computer
-import pystac_client
-import odc.stac
-import numpy as np
-from shapely.geometry import Point, shape
-from pyproj import Transformer
-
-bbox = [105.5, 9.2, 106.3, 10.0]
-time_range = "2023-01-01/2023-04-30"
-catalog = pystac_client.Client.open(
-    "https://planetarycomputer.microsoft.com/api/stac/v1",
-    modifier=planetary_computer.sign_inplace,
-)
-items = list(catalog.search(collections=["sentinel-2-l2a"], bbox=bbox, datetime=time_range, query={"eo:cloud_cover": {"lt": 30}}).items())
-items = sorted(items, key=lambda x: x.properties["eo:cloud_cover"])
-
-gdf = gpd.read_file("train/ST_training_data_updated_1130points_new.shp")
-gdf = gdf.to_crs("EPSG:32648")
-
-# Find a point that fails. Let's just test a few points.
-for idx, row in gdf.head(20).iterrows():
-    x_coord = row['geometry'].x
-    y_coord = row['geometry'].y
-    
-    transformer = Transformer.from_crs("epsg:32648", "epsg:4326", always_xy=True)
-    lon, lat = transformer.transform(x_coord, y_coord)
-    point = Point(lon, lat)
-    
-    filtered = []
-    for item in items:
-        if shape(item.geometry).contains(point):
-            filtered.append(item)
-            
-    filtered = [planetary_computer.sign(item) for item in filtered]
-    if not filtered:
-        print(f"Point {idx}: NO ITEMS CONTAINS POINT!")
-        continue
-        
-    ds = odc.stac.load(
-        filtered,
-        bands=["B02"],
-        x=(x_coord - 80, x_coord + 80),
-        y=(y_coord - 80, y_coord + 80),
-        crs="EPSG:32648",
-        resolution=10,
-        patch_url=planetary_computer.sign,
-        fail_on_error=False
-    ).compute()
-    
-    sums = ds["B02"].sum(dim=["x", "y"]).values
-    non_zero = (sums > 0).sum()
-    print(f"Point {idx}: {len(filtered)} items, {non_zero} non-zero time steps")

.temp/test_extract_one.py

@@ -1,37 +0,0 @@
-import geopandas as gpd
-import planetary_computer
-import pystac_client
-import odc.stac
-import sys
-
-bbox = [105.5, 9.2, 106.3, 10.0]
-time_range = "2023-01-01/2023-04-30"
-catalog = pystac_client.Client.open("https://planetarycomputer.microsoft.com/api/stac/v1", modifier=planetary_computer.sign_inplace)
-search = catalog.search(collections=["sentinel-2-l2a"], bbox=bbox, datetime=time_range, query={"eo:cloud_cover": {"lt": 30}})
-items = list(search.items())
-items = sorted(items, key=lambda x: x.properties.get("eo:cloud_cover", 100))[:4]
-items = [planetary_computer.sign(item) for item in items]
-
-gdf = gpd.read_file("train/ST_training_data_updated_1130points_new.shp")
-gdf = gdf.to_crs("EPSG:32648")
-
-row = gdf.iloc[0]
-x, y_coord = row.geometry.x, row.geometry.y
-point_bbox = [x - 80, y_coord - 80, x + 80, y_coord + 80]
-
-patch_s2 = odc.stac.load(
-    items,
-    bands=["B02", "B03", "B04", "B08", "SCL"],
-    x=(x - 80, x + 80),
-    y=(y_coord - 80, y_coord + 80),
-    crs="EPSG:32648",
-    resolution=10,
-    patch_url=planetary_computer.sign,
-    fail_on_error=False
-).compute()
-
-print("patch_s2 vars:", patch_s2.data_vars)
-if patch_s2.dims['x'] < 16 or patch_s2.dims['y'] < 16:
-    print("Too small:", patch_s2.dims)
-else:
-    print("Success dimension:", patch_s2.dims)

.temp/test_feature_extractor.py

@@ -1,171 +0,0 @@
-"""
-Test FeatureExtractor và kiểm tra tích hợp với hệ thống
-"""
-
-import numpy as np
-import xarray as xr
-from feature_extractor import get_feature_extractor
-from pathlib import Path
-
-print("=" * 70)
-print("TESTING FEATURE EXTRACTOR MODULE")
-print("=" * 70)
-
-# Test 1: Simple mode
-print("\n[TEST 1] Simple Mode (3 features)")
-print("-" * 50)
-extractor_simple = get_feature_extractor(mode='simple')
-print(f"✓ Created extractor: {extractor_simple.mode}")
-print(f"✓ Expected features: {extractor_simple.config['n_features']}")
-print(f"✓ Feature names: {extractor_simple.get_feature_names()}")
-
-# Create dummy NDVI data
-ndvi_dummy = xr.DataArray(
-    np.random.rand(10, 10),
-    dims=['y', 'x'],
-    coords={'y': np.arange(10), 'x': np.arange(10)}
-)
-vh_dummy = xr.DataArray(
-    np.random.rand(10, 10) * -10,
-    dims=['y', 'x'],
-    coords={'y': np.arange(10), 'x': np.arange(10)}
-)
-vv_dummy = xr.DataArray(
-    np.random.rand(10, 10) * -8,
-    dims=['y', 'x'],
-    coords={'y': np.arange(10), 'x': np.arange(10)}
-)
-
-features_simple = extractor_simple.extract(
-    ndvi_data=ndvi_dummy,
-    vh_data=vh_dummy,
-    vv_data=vv_dummy
-)
-print(f"✓ Extracted features shape: {features_simple.shape}")
-assert features_simple.shape[1] == 3, "Expected 3 features"
-print("✅ Simple mode test PASSED\n")
-
-# Test 2: Extended mode
-print("[TEST 2] Extended Mode (15 features)")
-print("-" * 50)
-extractor_extended = get_feature_extractor(mode='extended')
-print(f"✓ Created extractor: {extractor_extended.mode}")
-print(f"✓ Expected features: {extractor_extended.config['n_features']}")
-print(f"✓ Feature names: {extractor_extended.get_feature_names()}")
-
-# Create dummy S2 dataset with time dimension
-s2_dummy = xr.Dataset({
-    'B02': xr.DataArray(np.random.rand(5, 10, 10), dims=['time', 'y', 'x']),
-    'B03': xr.DataArray(np.random.rand(5, 10, 10), dims=['time', 'y', 'x']),
-    'B04': xr.DataArray(np.random.rand(5, 10, 10), dims=['time', 'y', 'x']),
-    'B08': xr.DataArray(np.random.rand(5, 10, 10), dims=['time', 'y', 'x']),
-    'B11': xr.DataArray(np.random.rand(5, 10, 10), dims=['time', 'y', 'x'])
-})
-
-features_extended = extractor_extended.extract(
-    s2_data=s2_dummy,
-    vh_data=vh_dummy,
-    vv_data=vv_dummy
-)
-print(f"✓ Extracted features shape: {features_extended.shape}")
-assert features_extended.shape[1] == 15, "Expected 15 features"
-print("✅ Extended mode test PASSED\n")
-
-# Test 3: Temporal mode
-print("[TEST 3] Temporal Mode (39 features for 12 timesteps)")
-print("-" * 50)
-extractor_temporal = get_feature_extractor(mode='temporal')
-print(f"✓ Created extractor: {extractor_temporal.mode}")
-
-# Create dummy S2 dataset with 12 timesteps
-s2_dummy_12 = xr.Dataset({
-    'B02': xr.DataArray(np.random.rand(12, 10, 10), dims=['time', 'y', 'x']),
-    'B03': xr.DataArray(np.random.rand(12, 10, 10), dims=['time', 'y', 'x']),
-    'B04': xr.DataArray(np.random.rand(12, 10, 10), dims=['time', 'y', 'x']),
-    'B08': xr.DataArray(np.random.rand(12, 10, 10), dims=['time', 'y', 'x']),
-    'B11': xr.DataArray(np.random.rand(12, 10, 10), dims=['time', 'y', 'x'])
-})
-
-features_temporal = extractor_temporal.extract(
-    s2_data=s2_dummy_12,
-    vh_data=vh_dummy,
-    vv_data=vv_dummy
-)
-
-# For temporal mode: 12 timesteps * 3 indices + 3 radar = 39 features
-expected_features = 12 * 3 + 3
-print(f"✓ Extracted features shape: {features_temporal.shape}")
-print(f"✓ Expected: {expected_features} features (12 timesteps * 3 indices + 3 radar)")
-
-feature_names_temporal = extractor_temporal.get_feature_names(n_timesteps=12)
-print(f"✓ Feature names count: {len(feature_names_temporal)}")
-print(f"✓ First 5 features: {feature_names_temporal[:5]}")
-print(f"✓ Last 5 features: {feature_names_temporal[-5:]}")
-
-assert features_temporal.shape[1] == expected_features, f"Expected {expected_features} features"
-assert len(feature_names_temporal) == expected_features, f"Expected {expected_features} feature names"
-print("✅ Temporal mode test PASSED\n")
-
-# Test 4: Check model_odc.joblib metadata
-print("[TEST 4] Verify model_odc.joblib metadata")
-print("-" * 50)
-metadata_file = Path("model_train/model_odc_info.json")
-if metadata_file.exists():
-    import json
-    with open(metadata_file) as f:
-        metadata = json.load(f)
-    
-    print(f"✓ Metadata file exists: {metadata_file}")
-    print(f"✓ Feature mode: {metadata.get('feature_mode')}")
-    print(f"✓ Number of features: {metadata.get('n_features')}")
-    print(f"✓ Features list length: {len(metadata.get('features', []))}")
-    print(f"✓ First 5 features: {metadata.get('features', [])[:5]}")
-    
-    assert metadata.get('feature_mode') == 'temporal', "Expected temporal mode"
-    assert metadata.get('n_features') == 39, "Expected 39 features"
-    assert len(metadata.get('features', [])) == 39, "Expected 39 feature names"
-    
-    print("✅ model_odc.joblib metadata VERIFIED\n")
-else:
-    print("❌ model_odc_info.json not found. Run: python create_odc_metadata.py")
-
-# Test 5: Check ModelManager integration
-print("[TEST 5] Test ModelManager integration")
-print("-" * 50)
-try:
-    from model_manager import get_model_manager
-    
-    manager = get_model_manager()
-    print(f"✓ ModelManager initialized")
-    
-    # List models
-    models = manager.list_models()
-    print(f"✓ Found {len(models)} models")
-    
-    # Check if model_odc.joblib has metadata
-    odc_model = next((m for m in models if m['filename'] == 'model_odc.joblib'), None)
-    if odc_model:
-        print(f"✓ model_odc.joblib found in list")
-        print(f"  - Feature mode: {odc_model.get('feature_mode', 'N/A')}")
-        print(f"  - N features: {odc_model.get('n_features', 'N/A')}")
-        print("✅ ModelManager integration test PASSED\n")
-    else:
-        print("⚠️  model_odc.joblib not in model list")
-        
-except Exception as e:
-    print(f"❌ ModelManager test failed: {e}")
-
-# Summary
-print("=" * 70)
-print("TEST SUMMARY")
-print("=" * 70)
-print("✅ All feature extraction modes working correctly")
-print("✅ Feature dimensions match expectations")
-print("✅ Feature names generated correctly")
-print("✅ model_odc.joblib metadata verified")
-print("\nNext steps:")
-print("1. Update api_server.py with run_prediction from run_prediction_new.py")
-print("2. Test training with different feature_modes")
-print("3. Test prediction with models using different modes")
-print("\nSee UPDATE_SUMMARY.md for details.")
-print("=" * 70)

.temp/test_gdf.py

@@ -1,3 +0,0 @@
-import geopandas as gpd
-gdf = gpd.read_file("train/ST_training_data_updated_1130points_new.shp")
-print(gdf.head(1)['HT_code'])

.temp/test_gdf_cols.py

@@ -1,3 +0,0 @@
-import geopandas as gpd
-gdf = gpd.read_file("train/ST_training_data_updated_1130points_new.shp")
-print(gdf.columns)

.temp/test_gpu.py

@@ -1,23 +0,0 @@
-import numpy as np
-from xgboost import XGBClassifier
-from sklearn.datasets import make_classification
-from sklearn.metrics import accuracy_score
-
-print("🚀 Testing XGBoost with CUDA GPU...")
-try:
-    X, y = make_classification(n_samples=10000, n_features=20, n_classes=2, random_state=42)
-    model = XGBClassifier(
-        n_estimators=100,
-        max_depth=10,
-        tree_method="hist",
-        device="cuda",
-        random_state=42,
-        verbosity=1
-    )
-    print("Training model...")
-    model.fit(X, y)
-    y_pred = model.predict(X)
-    acc = accuracy_score(y, y_pred)
-    print(f"✅ Training successful! Accuracy: {acc*100:.2f}%")
-except Exception as e:
-    print(f"❌ Error during training: {e}")

.temp/test_keys.py

@@ -1,3 +0,0 @@
-import torch
-checkpoint = torch.load('cloud_removal_model/cloud_removal_unet_best.pth', map_location='cpu')
-print(list(checkpoint['model_state_dict'].keys())[:5])

.temp/test_model_manager.py

@@ -1,101 +0,0 @@
-"""
-Test script for Model Manager
-Kiểm tra các chức năng: list models, load models, validate models
-"""
-
-from model_manager import ModelManager, get_model_manager
-import json
-
-def test_model_manager():
-    print("="*70)
-    print("MODEL MANAGER TEST")
-    print("="*70)
-    
-    # Initialize ModelManager
-    model_manager = get_model_manager()
-    print("\n✅ ModelManager initialized")
-    
-    # Test 1: List all models
-    print("\n" + "="*70)
-    print("TEST 1: LIST ALL MODELS")
-    print("="*70)
-    
-    models = model_manager.list_models()
-    print(f"\n📦 Found {len(models)} models:")
-    
-    for idx, model in enumerate(models, 1):
-        print(f"\n[{idx}] {model['filename']}")
-        print(f"    Size: {model['size_mb']:.2f} MB")
-        print(f"    Modified: {model['modified']}")
-        
-        if model.get('has_metadata'):
-            print(f"    Type: {model.get('model_type', 'N/A')}")
-            print(f"    Features: {model.get('n_features', 'N/A')}")
-            print(f"    Accuracy: {model.get('test_accuracy', 'N/A')}")
-            print(f"    Feature list: {model.get('features', [])}")
-        else:
-            print(f"    ⚠️  No metadata")
-    
-    # Test 2: Load a model
-    if len(models) > 0:
-        print("\n" + "="*70)
-        print("TEST 2: LOAD MODEL")
-        print("="*70)
-        
-        test_model = models[0]['filename']
-        print(f"\n🔄 Loading model: {test_model}")
-        
-        try:
-            model, encoder, metadata = model_manager.load_model(test_model)
-            print(f"✅ Model loaded successfully!")
-            print(f"\n📊 Metadata:")
-            print(json.dumps(metadata, indent=2))
-            
-            # Test 3: Validate model
-            print("\n" + "="*70)
-            print("TEST 3: VALIDATE MODEL")
-            print("="*70)
-            
-            validation = model_manager.validate_model(test_model)
-            print(f"\n✅ Validation result:")
-            print(f"  Valid: {validation['valid']}")
-            if validation['errors']:
-                print(f"  Errors: {validation['errors']}")
-            if validation['warnings']:
-                print(f"  Warnings: {validation['warnings']}")
-            
-            # Test 4: Get required features
-            print("\n" + "="*70)
-            print("TEST 4: GET REQUIRED FEATURES")
-            print("="*70)
-            
-            features = model_manager.get_required_features(test_model)
-            print(f"\n📋 Required features for {test_model}:")
-            for feat in features:
-                print(f"  - {feat}")
-            
-        except Exception as e:
-            print(f"❌ Error loading model: {e}")
-            import traceback
-            traceback.print_exc()
-    
-    # Test 5: Get latest model
-    print("\n" + "="*70)
-    print("TEST 5: GET LATEST MODEL")
-    print("="*70)
-    
-    latest = model_manager.get_latest_model()
-    print(f"\n📌 Latest model: {latest}")
-    
-    latest_xgb = model_manager.get_latest_model(model_type='xgboost')
-    print(f"📌 Latest XGBoost model: {latest_xgb}")
-    
-    latest_cnn = model_manager.get_latest_model(model_type='cnn')
-    print(f"📌 Latest CNN model: {latest_cnn}")
-    
-    print("\n" + "="*70)
-    print("✅ ALL TESTS COMPLETED")
-    print("="*70)
-
-if __name__ == "__main__":
-    test_model_manager()

.temp/test_model_struct.py

@@ -1,13 +0,0 @@
-import torch
-from pathlib import Path
-model = torch.load('cloud_removal_model/cloud_removal_unet_best.pth', map_location='cpu')
-print(type(model))
-print("hasattr inc:", hasattr(model, 'inc'))
-if hasattr(model, 'inc'):
-    print("hasattr double_conv:", hasattr(model.inc, 'double_conv'))
-    if hasattr(model.inc, 'double_conv'):
-        print("in_channels:", model.inc.double_conv[0].in_channels)
-else:
-    for name, param in model.named_parameters():
-        print(name, param.shape)
-        break

.temp/test_odc_stac.py

@@ -1,19 +0,0 @@
-import geopandas as gpd
-import planetary_computer
-import pystac_client
-import odc.stac
-
-bbox = [105.5, 9.2, 106.3, 10.0]
-time_range = "2023-01-01/2023-01-30"
-catalog = pystac_client.Client.open("https://planetarycomputer.microsoft.com/api/stac/v1", modifier=planetary_computer.sign_inplace)
-items = list(catalog.search(collections=["sentinel-2-l2a"], bbox=bbox, datetime=time_range).items())[:1]
-items = [planetary_computer.sign(item) for item in items]
-
-x = 561609
-y = 1024183
-try:
-    ds = odc.stac.load(items, bands=["B02"], crs="EPSG:32648", resolution=10, x=(x-80, x+80), y=(y-80, y+80))
-    print("Success with x/y:", ds.dims)
-except Exception as e:
-    print("Error with x/y:", e)
-

.temp/test_overlap.py

@@ -1,18 +0,0 @@
-import geopandas as gpd
-import planetary_computer
-import pystac_client
-import odc.stac
-import numpy as np
-
-bbox = [105.5, 9.2, 106.3, 10.0]
-time_range = "2023-01-01/2023-04-30"
-catalog = pystac_client.Client.open("https://planetarycomputer.microsoft.com/api/stac/v1", modifier=planetary_computer.sign_inplace)
-# Get ALL items
-items = list(catalog.search(collections=["sentinel-2-l2a"], bbox=bbox, datetime=time_range, query={"eo:cloud_cover": {"lt": 30}}).items())
-items = [planetary_computer.sign(item) for item in items]
-print(f"Total items: {len(items)}")
-
-x = 561609
-y = 1024183
-ds = odc.stac.load(items, bands=["B02"], x=(x-80, x+80), y=(y-80, y+80), crs="EPSG:32648", resolution=10, patch_url=planetary_computer.sign).compute()
-print("Time dimension size:", ds.dims['time'])

.temp/test_overlap2.py

@@ -1,20 +0,0 @@
-import geopandas as gpd
-import planetary_computer
-import pystac_client
-import odc.stac
-import numpy as np
-
-bbox = [105.5, 9.2, 106.3, 10.0]
-time_range = "2023-01-01/2023-04-30"
-catalog = pystac_client.Client.open("https://planetarycomputer.microsoft.com/api/stac/v1", modifier=planetary_computer.sign_inplace)
-items = list(catalog.search(collections=["sentinel-2-l2a"], bbox=bbox, datetime=time_range, query={"eo:cloud_cover": {"lt": 30}}).items())
-items = [planetary_computer.sign(item) for item in items]
-
-x = 561609
-y = 1024183
-ds = odc.stac.load(items, bands=["B02"], x=(x-80, x+80), y=(y-80, y+80), crs="EPSG:32648", resolution=10, patch_url=planetary_computer.sign, fail_on_error=False).compute()
-print("Original time size:", len(ds.time))
-ds2 = ds.dropna(dim="time", how="all")
-print("After dropna time size:", len(ds2.time))
-print("B02 mean:", np.nanmean(ds2["B02"].values))
-print("B02 non-nan count:", np.sum(~np.isnan(ds2["B02"].values)))

.temp/test_overlap_issue.py

@@ -1,25 +0,0 @@
-import geopandas as gpd
-import planetary_computer
-import pystac_client
-import odc.stac
-import numpy as np
-
-bbox = [105.5, 9.2, 106.3, 10.0]
-time_range = "2023-01-01/2023-04-30"
-catalog = pystac_client.Client.open("https://planetarycomputer.microsoft.com/api/stac/v1", modifier=planetary_computer.sign_inplace)
-items = list(catalog.search(collections=["sentinel-2-l2a"], bbox=bbox, datetime=time_range, query={"eo:cloud_cover": {"lt": 30}}).items())
-items = [planetary_computer.sign(item) for item in items]
-
-x = 561609
-y = 1024183
-ds = odc.stac.load(items, bands=["B02", "B03", "B04", "B08", "SCL"], x=(x-80, x+80), y=(y-80, y+80), crs="EPSG:32648", resolution=10, patch_url=planetary_computer.sign, fail_on_error=False).compute()
-
-print("Original shape:", ds["B02"].shape)
-ds2 = ds.dropna(dim="time", how="all")
-print("After dropna time size:", len(ds2.time))
-if len(ds2.time) > 0:
-    ds2 = ds2.isel(time=slice(0, 4))
-    median = ds2["B02"].median(dim="time", skipna=True).values
-    print("Median shape:", median.shape)
-    print("Zeros in median:", np.sum(median == 0) / median.size)
-    print("NaNs in median:", np.sum(np.isnan(median)) / median.size)

.temp/test_planetary.py

@@ -1,15 +0,0 @@
-import sys
-# Thêm đường dẫn hiện tại vào PYTHONPATH để import được new_import_ODC nếu cần
-sys.path.append('.')
-import warnings
-warnings.filterwarnings('ignore')
-from new_import_ODC import load_sen1
-
-print("Testing load_sen1 with a short time range to speed up Dask compute...")
-bbox = [105.5, 9.2, 106.4, 10.0]
-time_range = "2023-01-01/2023-01-31" # Short time range for fast testing
-vh, vv = load_sen1(bbox, time_range)
-print("VH shape:", vh.shape)
-print("VV shape:", vv.shape)
-print("VH CRS:", vh.rio.crs)
-print("Success!")

.temp/test_planetary2.py

@@ -1,49 +0,0 @@
-import warnings
-warnings.filterwarnings('ignore')
-
-def load_sen1(bbox, time_range):
-    import pystac_client
-    import planetary_computer
-    import odc.stac
-    
-    catalog = pystac_client.Client.open(
-        "https://planetarycomputer.microsoft.com/api/stac/v1",
-        modifier=planetary_computer.sign_inplace,
-    )
-
-    search = catalog.search(
-        collections=["sentinel-1-rtc"],
-        bbox=bbox,
-        datetime=time_range,
-    )
-    items = list(search.items())
-    print("Found items:", len(items))
-
-    ds_s1 = odc.stac.load(
-        items,
-        bands=["vv", "vh"],
-        bbox=bbox,
-        crs="EPSG:32648",
-        resolution=10,
-        chunks={"x": 2048, "y": 2048, "time": 1}
-    )
-
-    ds_median = ds_s1.median(dim="time").compute()
-    vv = ds_median["vv"]
-    vh = ds_median["vh"]
-
-    vv = vv.expand_dims(dim="band")
-    vh = vh.expand_dims(dim="band")
-
-    vv = vv.rio.write_crs("EPSG:32648")
-    vh = vh.rio.write_crs("EPSG:32648")
-
-    return vh, vv
-
-print("Testing load_sen1...")
-bbox = [105.5, 9.2, 106.4, 10.0]
-time_range = "2022-09-01/2023-10-01"
-vh, vv = load_sen1(bbox, time_range)
-print("VH shape:", vh.shape)
-print("VV shape:", vv.shape)
-print("Success!")

.temp/test_planetary_computer.py

@@ -1,88 +0,0 @@
-"""
-Test Microsoft Planetary Computer connectivity và token
-"""
-import planetary_computer
-from pystac_client import Client
-from datetime import datetime, timedelta
-
-print("=" * 70)
-print("🧪 TESTING MICROSOFT PLANETARY COMPUTER CONNECTION")
-print("=" * 70)
-
-# Test 1: Basic connection
-print("\n1️⃣ Testing basic connection...")
-try:
-    catalog = Client.open(
-        "https://planetarycomputer.microsoft.com/api/stac/v1",
-        modifier=planetary_computer.sign_inplace,
-    )
-    print("✅ Successfully connected to Planetary Computer")
-    print(f"   Catalog ID: {catalog.id}")
-    print(f"   Title: {catalog.title}")
-except Exception as e:
-    print(f"❌ Connection failed: {e}")
-    exit(1)
-
-# Test 2: List collections
-print("\n2️⃣ Testing collections access...")
-try:
-    collections = list(catalog.get_collections())
-    print(f"✅ Found {len(collections)} collections")
-    sentinel_2 = [c for c in collections if 'sentinel-2' in c.id.lower()]
-    print(f"   Sentinel-2 collections: {[c.id for c in sentinel_2]}")
-except Exception as e:
-    print(f"❌ Collections access failed: {e}")
-
-# Test 3: Small search query (very conservative)
-print("\n3️⃣ Testing small search query...")
-try:
-    # Tiny bbox in Vietnam
-    bbox = [105.8, 10.0, 105.9, 10.1]  # ~10km x 10km area
-    end_date = datetime.now()
-    start_date = end_date - timedelta(days=7)  # Last 7 days only
-    
-    time_range = f"{start_date.strftime('%Y-%m-%d')}/{end_date.strftime('%Y-%m-%d')}"
-    
-    print(f"   Bbox: {bbox}")
-    print(f"   Time: {time_range}")
-    print(f"   Searching...")
-    
-    search = catalog.search(
-        collections=["sentinel-2-l2a"],
-        bbox=bbox,
-        datetime=time_range,
-        limit=5  # Only 5 items
-    )
-    
-    items = []
-    for i, item in enumerate(search.items()):
-        items.append(item)
-        if i >= 4:  # Stop at 5
-            break
-    
-    print(f"✅ Search successful! Found {len(items)} items")
-    if items:
-        first_item = items[0]
-        print(f"   First item: {first_item.id}")
-        print(f"   Date: {first_item.datetime}")
-        
-        # Test token signing
-        signed_item = planetary_computer.sign(first_item)
-        print(f"✅ SAS token signing works")
-        print(f"   Asset keys: {list(signed_item.assets.keys())[:5]}")
-        
-except Exception as e:
-    print(f"❌ Search failed: {e}")
-    import traceback
-    traceback.print_exc()
-
-print("\n" + "=" * 70)
-print("🏁 Test completed!")
-print("=" * 70)
-print("\n💡 Nếu test này PASS:")
-print("   → Planetary Computer hoạt động bình thường")
-print("   → Vấn đề là query quá lớn (bbox/time range/max_scenes)")
-print("\n💡 Nếu test này FAIL:")
-print("   → Kiểm tra internet connection")
-print("   → Thử lại sau (server có thể bị quá tải)")
-print("   → Xem xét dùng dữ liệu local")

.temp/test_raw.py

@@ -1,17 +0,0 @@
-import geopandas as gpd
-import planetary_computer
-import pystac_client
-import odc.stac
-import numpy as np
-
-bbox = [105.5, 9.2, 106.3, 10.0]
-time_range = "2023-01-01/2023-04-30"
-catalog = pystac_client.Client.open("https://planetarycomputer.microsoft.com/api/stac/v1", modifier=planetary_computer.sign_inplace)
-items = list(catalog.search(collections=["sentinel-2-l2a"], bbox=bbox, datetime=time_range, query={"eo:cloud_cover": {"lt": 30}}).items())[:4]
-items = [planetary_computer.sign(item) for item in items]
-
-x = 561609
-y = 1024183
-ds = odc.stac.load(items, bands=["B02", "B03", "B04", "B08"], x=(x-80, x+80), y=(y-80, y+80), crs="EPSG:32648", resolution=10, patch_url=planetary_computer.sign).compute()
-print("B04 nanmean:", np.nanmean(ds["B04"].values))
-print("B04 nanmax:", np.nanmax(ds["B04"].values))

.temp/test_shape.py

@@ -1,41 +0,0 @@
-import new_import_ODC
-importlib = __import__('importlib')
-importlib.reload(new_import_ODC)
-from new_import_ODC import *
-import numpy as np
-
-date_range = ("2022-09-01", "2022-10-01")
-longtitude_range = (105.86, 105.94)
-latitude_range = (9.65, 9.69)
-coordinates = (longtitude_range, latitude_range)
-
-print("Loading S2...")
-data = load_data(None, date_range, longtitude_range, latitude_range)
-result = mask_clean(data)
-ds1 = calculate_indices(result, index="NDVI", satellite_mission="s2")
-ndvi = ds1["NDVI"]
-time_split = [
-    slice("2022-09-01", "2023-01-01"),
-    slice("2023-01-01", "2023-05-01"),
-    slice("2023-05-01", "2023-07-01"),
-    slice("2023-07-01", "2022-10-01"),
-]
-fill_nan_ndvi = fill_nan(ndvi, time_split)
-average_ndvi = fill_nan_ndvi.resample(time="1M").mean().compute()
-
-print("Loading S1...")
-dsvh, dsvv = load_data_sen1(None, date_range, coordinates)
-average_vv = calculate_average(dsvv, time_pattern='1M')
-average_vh = calculate_average(dsvh, time_pattern='1M')
-
-train = load_train_data("train/ST_training_data_updated_1130points_new.shp")
-point = train.iloc[0]
-
-ndvi_val = average_ndvi.sel(x=point.geometry.x, y=point.geometry.y, method='nearest').values
-vh_val = average_vh.sel(x=point.geometry.x, y=point.geometry.y, method='nearest').values
-vv_val = average_vv.sel(x=point.geometry.x, y=point.geometry.y, method='nearest').values
-
-print("NDVI shape:", ndvi_val.shape, "ndim:", ndvi_val.ndim)
-print("VH shape:", vh_val.shape, "ndim:", vh_val.ndim)
-print("VV shape:", vv_val.shape, "ndim:", vv_val.ndim)
-

.temp/test_spatial_filter.py

@@ -1,42 +0,0 @@
-import geopandas as gpd
-import planetary_computer
-import pystac_client
-import odc.stac
-import numpy as np
-import time
-from shapely.geometry import Point, box, shape
-
-bbox = [105.5, 9.2, 106.3, 10.0]
-time_range = "2023-01-01/2023-04-30"
-catalog = pystac_client.Client.open("https://planetarycomputer.microsoft.com/api/stac/v1", modifier=planetary_computer.sign_inplace)
-items = list(catalog.search(collections=["sentinel-2-l2a"], bbox=bbox, datetime=time_range, query={"eo:cloud_cover": {"lt": 30}}).items())
-
-x = 561609
-y = 1024183
-
-start = time.time()
-# Filter items by spatial intersection
-from pyproj import Transformer
-# The items geometry are in EPSG:4326 (lon, lat)
-# Our x, y are in EPSG:32648
-transformer = Transformer.from_crs("epsg:32648", "epsg:4326", always_xy=True)
-lon, lat = transformer.transform(x, y)
-point = Point(lon, lat)
-
-filtered_items = []
-for item in items:
-    geom = shape(item.geometry)
-    if geom.contains(point):
-        filtered_items.append(item)
-
-filtered_items = sorted(filtered_items, key=lambda x: x.properties["eo:cloud_cover"])
-
-print("Original items:", len(items))
-print("Filtered items:", len(filtered_items))
-print("Time to filter:", time.time() - start)
-
-start = time.time()
-filtered_items = [planetary_computer.sign(item) for item in filtered_items]
-ds = odc.stac.load(filtered_items[:4], bands=["B02"], x=(x-80, x+80), y=(y-80, y+80), crs="EPSG:32648", resolution=10, patch_url=planetary_computer.sign, fail_on_error=False).compute()
-print("Time to load 4 items:", time.time() - start)
-print(ds["B02"].shape)

.temp/test_training_api.py

@@ -1,113 +0,0 @@
-#!/usr/bin/env python3
-"""
-Test script to verify training API endpoints
-"""
-
-import requests
-import json
-
-API_BASE = "http://localhost:8000/api"
-
-def test_training_labels():
-    """Test /api/training/labels endpoint"""
-    print("=" * 70)
-    print("TEST 1: Getting training labels")
-    print("=" * 70)
-    
-    response = requests.get(f"{API_BASE}/training/labels")
-    if response.ok:
-        data = response.json()
-        print(f"✅ Success! Found {data['count']} labels:")
-        for label in data['labels']:
-            print(f"  {label['code']}: {label['name']}")
-    else:
-        print(f"❌ Error: {response.status_code}")
-    print()
-
-def test_training_files():
-    """Test /api/training/files endpoint"""
-    print("=" * 70)
-    print("TEST 2: Getting training files")
-    print("=" * 70)
-    
-    response = requests.get(f"{API_BASE}/training/files")
-    if response.ok:
-        data = response.json()
-        print(f"✅ Success! Found {data['count']} training files:")
-        for file in data['files']:
-            print(f"\n  📄 {file['filename']}")
-            print(f"     Size: {file['size_mb']} MB")
-            if 'point_count' in file:
-                print(f"     Points: {file['point_count']}")
-                print(f"     Label column: {file.get('label_column', 'N/A')}")
-                print(f"     Unique labels: {file.get('label_count', 0)}")
-    else:
-        print(f"❌ Error: {response.status_code}")
-    print()
-
-def test_shapefile_labels(filename="ST_training data_updated_1130points_new.shp"):
-    """Test /api/training/shapefile/{filename}/labels endpoint"""
-    print("=" * 70)
-    print(f"TEST 3: Getting labels from shapefile: {filename}")
-    print("=" * 70)
-    
-    response = requests.get(f"{API_BASE}/training/shapefile/{filename}/labels")
-    if response.ok:
-        data = response.json()
-        print(f"✅ Success!")
-        print(f"  Filename: {data['filename']}")
-        print(f"  Points: {data['point_count']}")
-        print(f"  Label column: {data['label_column']}")
-        print(f"  Unique labels: {data['label_count']}")
-        print(f"  Bbox: {data['bbox']}")
-        print(f"\n  Labels distribution:")
-        for label in data['labels']:
-            mapped = "✅" if label['mapped'] else "⚠️"
-            print(f"    {mapped} {label['name']}: {label['count']} points (code: {label['code']})")
-    else:
-        print(f"❌ Error: {response.status_code}")
-        print(response.text)
-    print()
-
-def test_config_presets():
-    """Test /api/config/presets endpoint"""
-    print("=" * 70)
-    print("TEST 4: Getting config presets")
-    print("=" * 70)
-    
-    response = requests.get(f"{API_BASE}/config/presets")
-    if response.ok:
-        data = response.json()
-        print(f"✅ Success! Found {len(data['presets'])} presets:")
-        for preset in data['presets']:
-            print(f"\n  📋 {preset['name']}")
-            config = preset['config']
-            print(f"     Bbox: [{config['min_lon']}, {config['min_lat']}, {config['max_lon']}, {config['max_lat']}]")
-            print(f"     Time: {config['start_date']} → {config['end_date']}")
-            print(f"     Resolution: {config['resolution']}m")
-    else:
-        print(f"❌ Error: {response.status_code}")
-    print()
-
-if __name__ == "__main__":
-    print("\n" + "=" * 70)
-    print("🧪 TESTING TRAINING API ENDPOINTS")
-    print("=" * 70 + "\n")
-    
-    try:
-        test_training_labels()
-        test_training_files()
-        test_shapefile_labels()
-        test_config_presets()
-        
-        print("=" * 70)
-        print("✅ ALL TESTS COMPLETED!")
-        print("=" * 70)
-        
-    except requests.exceptions.ConnectionError:
-        print("\n❌ Error: Cannot connect to API server")
-        print("Make sure the server is running: python api_server.py")
-    except Exception as e:
-        print(f"\n❌ Error: {e}")
-        import traceback
-        traceback.print_exc()

.temp/test_unet.py

@@ -1,5 +0,0 @@
-from train_cloud_removal import UNet
-model = UNet(in_channels=6, out_channels=4)
-print(hasattr(model, 'inc'))
-print(hasattr(model, 'conv1'))
-print(list(model.parameters())[0].shape)

.temp/test_y_mapped.py

@@ -1,13 +0,0 @@
-import numpy as np
-
-y = []
-# simulate appending 1130 labels
-for i in range(1130):
-    y.append(i % 5)
-
-y = np.array(y)
-unique_labels = sorted(list(np.unique(y)))
-label_map = {lbl: i for i, lbl in enumerate(unique_labels)}
-y_mapped = np.array([label_map[l] for l in y])
-
-print(len(y), len(y_mapped))

01.train_ODC.ipynb

@@ -0,0 +1,947 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "912ed572-1658-406b-976c-cd6de2d4e89e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'easi_tools'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "File \u001b[0;32m<timed exec>:4\u001b[0m\n",
+      "File \u001b[0;32m~/CSIROBoeingPhase5-Vietnam/new_import_ODC.py:23\u001b[0m\n\u001b[1;32m     21\u001b[0m easinotebooksrepo \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m/home/jovyan/easi-notebooks\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m     22\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m easinotebooksrepo \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m sys\u001b[38;5;241m.\u001b[39mpath: sys\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mappend(easinotebooksrepo)\n\u001b[0;32m---> 23\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01measi_tools\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m EasiDefaults, xarray_object_size, notebook_utils, unset_cachingproxy\n\u001b[1;32m     24\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01measi_tools\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mload_s2l2a\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m load_s2l2a_with_offset\n\u001b[1;32m     25\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mdask\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdistributed\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m progress\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'easi_tools'"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import importlib\n",
+    "import new_import_ODC  \n",
+    "\n",
+    "importlib.reload(new_import_ODC)\n",
+    "\n",
+    "from new_import_ODC import *\n",
+    "\n",
+    "print(\"✅ All modules loaded successfully\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "d824dc4f-994b-4d1c-8d24-ce6674da141c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ AWS credentials loaded from environment variables\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/x79/miniconda/envs/env_01/lib/python3.10/site-packages/distributed/node.py:187: UserWarning: Port 8787 is already in use.\n",
+      "Perhaps you already have a cluster running?\n",
+      "Hosting the HTTP server on port 41709 instead\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Dask cluster initialized\n",
+      "   Cluster: LocalCluster(9f2167a3, 'tcp://127.0.0.1:41233', workers=4, threads=24, memory=31.26 GiB)\n",
+      "✅ Datacube connected (metadata only)\n",
+      "\n",
+      "======================================================================\n",
+      "CPU times: user 4.98 s, sys: 831 ms, total: 5.81 s\n",
+      "Wall time: 7.69 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "import os\n",
+    "import sys\n",
+    "\n",
+    "print(\"✅ AWS credentials loaded from environment variables\")\n",
+    "\n",
+    "# Cấu hình Dask local\n",
+    "from dask.distributed import Client, LocalCluster\n",
+    "\n",
+    "cluster = LocalCluster(n_workers=4)\n",
+    "client = Client(cluster)\n",
+    "print(\"✅ Dask cluster initialized\")\n",
+    "print(f\"   Cluster: {cluster}\")\n",
+    "\n",
+    "# Khai báo Datacube (chỉ để lấy metadata, không dùng load())\n",
+    "import datacube\n",
+    "try:\n",
+    "    dc = datacube.Datacube()\n",
+    "    print(\"✅ Datacube connected (metadata only)\")\n",
+    "except Exception as e:\n",
+    "    print(f\"⚠️  Datacube connection not critical: {e}\")\n",
+    "    dc = None\n",
+    "\n",
+    "print(\"\\n\" + \"=\"*70)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "1e113730",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "======================================================================\n",
+      "GETTING SENTINEL-2 SCENE METADATA\n",
+      "======================================================================\n",
+      "\n",
+      "[1] Loading metadata from datacube...\n",
+      "    ✅ Found 40 scenes\n",
+      "\n",
+      "[2] Selected scene: S2A_48PWR_20231226_0_L2A\n",
+      "    Date: 2023-12-26 03:35:26.919000+00:00\n",
+      "\n",
+      "[3] Available bands:\n",
+      "    - nir: https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/48/P/WR/20\n",
+      "    - red: https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/48/P/WR/20\n",
+      "    - scl: https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/48/P/WR/20\n",
+      "    - blue: https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/48/P/WR/20\n",
+      "    - green: https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/48/P/WR/20\n",
+      "    - nir08: https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/48/P/WR/20\n",
+      "    - nir09: https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/48/P/WR/20\n",
+      "    - swir16: https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/48/P/WR/20\n",
+      "    - swir22: https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/48/P/WR/20\n",
+      "    - coastal: https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/48/P/WR/20\n",
+      "    - rededge1: https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/48/P/WR/20\n",
+      "    - rededge2: https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/48/P/WR/20\n",
+      "    - rededge3: https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/48/P/WR/20\n",
+      "======================================================================\n",
+      "CPU times: user 3.34 s, sys: 76.3 ms, total: 3.41 s\n",
+      "Wall time: 3.23 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "# 🔧 Get Sentinel-2 scene metadata from datacube\n",
+    "print(\"=\"*70)\n",
+    "print(\"GETTING SENTINEL-2 SCENE METADATA\")\n",
+    "print(\"=\"*70)\n",
+    "\n",
+    "date_range = (\"2023-03-01\", \"2023-12-31\")\n",
+    "longtitude_range = (105.5, 106.4)\n",
+    "latitude_range = (9.2, 10.0)\n",
+    "\n",
+    "try:\n",
+    "    print(f\"\\n[1] Loading metadata from datacube...\")\n",
+    "    datasets = list(dc.find_datasets(product='s2_l2a', time=date_range))\n",
+    "    print(f\"    ✅ Found {len(datasets)} scenes\")\n",
+    "    \n",
+    "    if datasets:\n",
+    "        selected = datasets[0]\n",
+    "        print(f\"\\n[2] Selected scene: {selected.metadata.label}\")\n",
+    "        scene_datetime = selected.time.begin if hasattr(selected.time, 'begin') else selected.time\n",
+    "        print(f\"    Date: {scene_datetime}\")\n",
+    "        \n",
+    "        # Display measurement paths\n",
+    "        print(f\"\\n[3] Available bands:\")\n",
+    "        for name, measurement in selected.measurements.items():\n",
+    "            print(f\"    - {name}: {measurement['path'][:80]}\")\n",
+    "            \n",
+    "except Exception as e:\n",
+    "    print(f\"❌ Error: {e}\")\n",
+    "    import traceback\n",
+    "    traceback.print_exc()\n",
+    "\n",
+    "print(\"=\"*70)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "3cd69645",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "======================================================================\n",
+      "CHECKING FOR CACHED DATASET\n",
+      "======================================================================\n",
+      "\n",
+      "⏳ Cache file not found: dataset_cache/sentinel2_timeseries_40scenes.nc\n",
+      "   Will download from S3 and save cache\n",
+      "   (Next run will use cache automatically)\n",
+      "======================================================================\n",
+      "CPU times: user 4.36 ms, sys: 3.7 ms, total: 8.06 ms\n",
+      "Wall time: 7.22 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "# 🔍 CHECK IF DATASET CACHE EXISTS (Skip download if available)\n",
+    "print(\"=\"*70)\n",
+    "print(\"CHECKING FOR CACHED DATASET\")\n",
+    "print(\"=\"*70)\n",
+    "\n",
+    "import os\n",
+    "import xarray as xr\n",
+    "\n",
+    "cache_dir = \"dataset_cache\"\n",
+    "cache_file = f\"{cache_dir}/sentinel2_timeseries_40scenes.nc\"\n",
+    "\n",
+    "use_cache = False\n",
+    "\n",
+    "if os.path.exists(cache_file):\n",
+    "    print(f\"\\n✅ Cache file found: {cache_file}\")\n",
+    "    \n",
+    "    # Get file info\n",
+    "    file_size_gb = os.path.getsize(cache_file) / (1024**3)\n",
+    "    print(f\"   File size: {file_size_gb:.2f} GB\")\n",
+    "    \n",
+    "    # Try to load\n",
+    "    try:\n",
+    "        print(f\"\\n🔄 Loading dataset from cache...\")\n",
+    "        data = xr.open_dataset(cache_file)\n",
+    "        \n",
+    "        print(f\"✅ Dataset loaded from cache!\")\n",
+    "        print(f\"   Total scenes: {len(data['time'])}\")\n",
+    "        print(f\"   Variables: {len(data.data_vars)}\")\n",
+    "        print(f\"   Dimensions: {dict(data.dims)}\")\n",
+    "        print(f\"\\n   ⏭️  Skipping S3 download (using cached data)\")\n",
+    "        \n",
+    "        use_cache = True\n",
+    "        \n",
+    "    except Exception as e:\n",
+    "        print(f\"❌ Error loading cache: {e}\")\n",
+    "        print(f\"   Will download fresh data from S3\")\n",
+    "        use_cache = False\n",
+    "else:\n",
+    "    print(f\"\\n⏳ Cache file not found: {cache_file}\")\n",
+    "    print(f\"   Will download from S3 and save cache\")\n",
+    "    print(f\"   (Next run will use cache automatically)\")\n",
+    "\n",
+    "print(\"=\"*70)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "435f9f78-a9a4-4226-86ca-d4bec42d454e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "======================================================================\n",
+      "LOADING SENTINEL-2 DATA FROM S3 COGs (RASTERIO) - OPTIMAL ACCURACY\n",
+      "======================================================================\n",
+      "\n",
+      "📥 Downloading from S3...\n",
+      "\n",
+      "📦 Found 40 available scenes\n",
+      "   Date range: 2023-03-01 to 2023-12-31\n",
+      "\n",
+      "[LOADING] Loading ALL 40 scenes with ALL available bands...\n",
+      "   (Keeping NATIVE resolution - NO upsampling/magnification)\n",
+      "   Available bands: ['nir', 'red', 'scl', 'blue', 'green', 'nir08', 'nir09', 'swir16', 'swir22', 'coastal', 'rededge1', 'rededge2', 'rededge3']\n",
+      "\n",
+      "   [ 1/1] S2A_48PWR_20231226_0_L2A (2023-12-26)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      ✅ 13 bands loaded\n",
+      "\n",
+      "✅ Successfully loaded 1 scenes!\n",
+      "\n",
+      "[RESOLUTION NORMALIZATION] Aligning all bands to native resolution (NO magnification)...\n",
+      "   Reference resolution: 10980×10980 pixels (native nir)\n",
+      "      Resampling scl: 5490×5490 → 10980×10980\n",
+      "      Resampling nir08: 5490×5490 → 10980×10980\n",
+      "      Resampling nir09: 1830×1830 → 10980×10980\n",
+      "      Resampling swir16: 5490×5490 → 10980×10980\n",
+      "      Resampling swir22: 5490×5490 → 10980×10980\n",
+      "      Resampling coastal: 1830×1830 → 10980×10980\n",
+      "      Resampling rededge1: 5490×5490 → 10980×10980\n",
+      "      Resampling rededge2: 5490×5490 → 10980×10980\n",
+      "      Resampling rededge3: 5490×5490 → 10980×10980\n",
+      "✅ Resolution normalization complete! (9 bands resampled)\n",
+      "\n",
+      "[SPECTRAL INDICES] Calculating spectral indices for each scene...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<timed exec>:169: RuntimeWarning: divide by zero encountered in divide\n",
+      "<timed exec>:169: RuntimeWarning: invalid value encountered in divide\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Calculated 2 spectral indices per scene\n",
+      "\n",
+      "[STACKING] Stacking all 1 scenes to create time-series...\n",
+      "\n",
+      "[TEMPORAL FEATURES] Computing temporal features from time-series...\n",
+      "✅ Added 6 temporal/aggregate features\n",
+      "\n",
+      "[CACHE] Saving dataset to cache...\n",
+      "✅ Dataset saved to cache: dataset_cache/sentinel2_timeseries_40scenes.nc\n",
+      "   Cache size: 6.40 GB\n",
+      "\n",
+      "✅ OPTIMAL Dataset with native resolution + temporal features created!\n",
+      "   ======================================================================\n",
+      "   🎬 Total scenes (time steps): 1\n",
+      "   📊 Total bands/variables: 21\n",
+      "   🖼️  Spatial size: 10980 × 10980 pixels (NATIVE resolution)\n",
+      "   📏 Native resolution: 10m (Sentinel-2 L2A)\n",
+      "   ⏰ Temporal range: 2023-12-26 to 2023-12-26\n",
+      "❌ Error: name 'notebook_utils' is not defined\n",
+      "======================================================================\n",
+      "CPU times: user 9min, sys: 4min 16s, total: 13min 16s\n",
+      "Wall time: 16min 42s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Traceback (most recent call last):\n",
+      "  File \"<timed exec>\", line 262, in <module>\n",
+      "NameError: name 'notebook_utils' is not defined\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "# 💾 LOAD SENTINEL-2 DATA DIRECTLY FROM S3 COGS USING RASTERIO - WITH TEMPORAL FEATURES\n",
+    "print(\"=\"*70)\n",
+    "print(\"LOADING SENTINEL-2 DATA FROM S3 COGs (RASTERIO) - OPTIMAL ACCURACY\")\n",
+    "print(\"=\"*70)\n",
+    "\n",
+    "try:\n",
+    "    import rasterio\n",
+    "    import xarray as xr\n",
+    "    import numpy as np\n",
+    "    from scipy import ndimage\n",
+    "    \n",
+    "    # ===== CHECK IF SHOULD SKIP DOWNLOAD =====\n",
+    "    if use_cache and data is not None:\n",
+    "        print(f\"\\n✅ Using cached dataset - skipping download!\")\n",
+    "        print(f\"   Variables: {len(data.data_vars)}\")\n",
+    "        print(f\"   Shape: {data.dims}\")\n",
+    "        display(data)\n",
+    "    \n",
+    "    else:\n",
+    "        # ===== DOWNLOAD FROM S3 =====\n",
+    "        print(f\"\\n📥 Downloading from S3...\")\n",
+    "        \n",
+    "        # Get all scenes from datacube metadata\n",
+    "        datasets = list(dc.find_datasets(\n",
+    "            product='s2_l2a',\n",
+    "            time=date_range\n",
+    "        ))\n",
+    "        \n",
+    "        if not datasets:\n",
+    "            raise ValueError(\"No datasets found for date range\")\n",
+    "        \n",
+    "        print(f\"\\n📦 Found {len(datasets)} available scenes\")\n",
+    "        print(f\"   Date range: {date_range[0]} to {date_range[1]}\")\n",
+    "        \n",
+    "        # ===== LOAD ALL SCENES WITH ALL AVAILABLE BANDS (NO MAGNIFICATION) =====\n",
+    "        print(f\"\\n[LOADING] Loading ALL {len(datasets)} scenes with ALL available bands...\")\n",
+    "        print(f\"   (Keeping NATIVE resolution - NO upsampling/magnification)\")\n",
+    "        \n",
+    "        # num_scenes = len(datasets)  # Load ALL scenes\n",
+    "        num_scenes = 1 # Load ALL scenes\n",
+    "        all_data_dict = {}\n",
+    "        failed_scenes = []\n",
+    "        scene_dates = []\n",
+    "        \n",
+    "        # Discover all available bands from first scene\n",
+    "        first_scene = datasets[0]\n",
+    "        all_available_bands = list(first_scene.measurements.keys())\n",
+    "        print(f\"   Available bands: {all_available_bands}\")\n",
+    "        \n",
+    "        for scene_idx in range(num_scenes):\n",
+    "            selected = datasets[scene_idx]\n",
+    "            scene_label = selected.metadata.label\n",
+    "            scene_datetime = selected.time.begin if hasattr(selected.time, 'begin') else selected.time\n",
+    "            scene_dates.append(scene_datetime)\n",
+    "            \n",
+    "            # Print progress every 5 scenes\n",
+    "            if scene_idx % 5 == 0 or scene_idx == 0 or scene_idx == num_scenes - 1:\n",
+    "                print(f\"\\n   [{scene_idx + 1:2d}/{num_scenes}] {scene_label} ({scene_datetime.date()})\")\n",
+    "            \n",
+    "            # Load ALL available bands from S3 COGs\n",
+    "            scene_data_dict = {}\n",
+    "            \n",
+    "            for band_name in all_available_bands:\n",
+    "                if band_name in selected.measurements:\n",
+    "                    band_path = selected.measurements[band_name]['path']\n",
+    "                    \n",
+    "                    try:\n",
+    "                        with rasterio.open(band_path) as src:\n",
+    "                            data_band = src.read(1)\n",
+    "                            scene_data_dict[band_name] = data_band\n",
+    "                    except Exception as e:\n",
+    "                        if scene_idx % 5 == 0:\n",
+    "                            print(f\"      ⚠️ Error loading {band_name}: {str(e)[:30]}\")\n",
+    "                        failed_scenes.append((scene_idx, scene_label, band_name, str(e)))\n",
+    "            \n",
+    "            if scene_data_dict:\n",
+    "                all_data_dict[scene_idx] = scene_data_dict\n",
+    "                if scene_idx % 5 == 0 or scene_idx == num_scenes - 1:\n",
+    "                    print(f\"      ✅ {len(scene_data_dict)} bands loaded\")\n",
+    "            else:\n",
+    "                failed_scenes.append((scene_idx, scene_label, \"all\", \"No bands loaded\"))\n",
+    "        \n",
+    "        if not all_data_dict:\n",
+    "            raise ValueError(\"Could not load any bands from any scene\")\n",
+    "        \n",
+    "        print(f\"\\n✅ Successfully loaded {len(all_data_dict)} scenes!\")\n",
+    "        if failed_scenes:\n",
+    "            print(f\"⚠️  Failed to load {len(failed_scenes)} band instances (will be skipped)\")\n",
+    "        \n",
+    "        # ===== NORMALIZE RESOLUTION (No upsampling - just match to highest) =====\n",
+    "        print(f\"\\n[RESOLUTION NORMALIZATION] Aligning all bands to native resolution (NO magnification)...\")\n",
+    "        \n",
+    "        # Find max resolution\n",
+    "        ref_resolution = None\n",
+    "        max_size = 0\n",
+    "        max_band = None\n",
+    "        \n",
+    "        for scene_idx in all_data_dict.keys():\n",
+    "            for band_name, data_band in all_data_dict[scene_idx].items():\n",
+    "                size = data_band.shape[0]\n",
+    "                if size > max_size:\n",
+    "                    max_size = size\n",
+    "                    ref_resolution = size\n",
+    "                    max_band = band_name\n",
+    "        \n",
+    "        print(f\"   Reference resolution: {max_size}×{max_size} pixels (native {max_band})\")\n",
+    "        \n",
+    "        # Resample all bands to match reference resolution (both up and down)\n",
+    "        resampled_count = 0\n",
+    "        for scene_idx in all_data_dict.keys():\n",
+    "            for band_name in list(all_data_dict[scene_idx].keys()):\n",
+    "                band_data_arr = all_data_dict[scene_idx][band_name]\n",
+    "                current_size = band_data_arr.shape[0]\n",
+    "                \n",
+    "                if current_size != ref_resolution:\n",
+    "                    scale_factor = ref_resolution / current_size\n",
+    "                    \n",
+    "                    # Resample to match reference resolution (both up and down)\n",
+    "                    if band_name == 'scl':\n",
+    "                        resampled_data = ndimage.zoom(band_data_arr, scale_factor, order=0)\n",
+    "                    else:\n",
+    "                        resampled_data = ndimage.zoom(band_data_arr, scale_factor, order=1)\n",
+    "                    \n",
+    "                    all_data_dict[scene_idx][band_name] = resampled_data\n",
+    "                    new_size = resampled_data.shape[0]\n",
+    "                    if scene_idx == 0:  # Print for first scene only to reduce clutter\n",
+    "                        print(f\"      Resampling {band_name}: {current_size}×{current_size} → {new_size}×{new_size}\")\n",
+    "                    resampled_count += 1\n",
+    "        \n",
+    "        print(f\"✅ Resolution normalization complete! ({resampled_count} bands resampled)\")\n",
+    "        \n",
+    "        # ===== CALCULATE SPECTRAL INDICES FOR EACH SCENE =====\n",
+    "        print(f\"\\n[SPECTRAL INDICES] Calculating spectral indices for each scene...\")\n",
+    "        \n",
+    "        indices_count = 0\n",
+    "        for scene_idx in all_data_dict.keys():\n",
+    "            scene_data = all_data_dict[scene_idx]\n",
+    "            \n",
+    "            try:\n",
+    "                # NDVI: (NIR - Red) / (NIR + Red)\n",
+    "                if 'nir' in scene_data and 'red' in scene_data:\n",
+    "                    nir = scene_data['nir'].astype(float)\n",
+    "                    red = scene_data['red'].astype(float)\n",
+    "                    ndvi = (nir - red) / (nir + red + 1e-8)\n",
+    "                    scene_data['ndvi'] = ndvi.astype(np.float32)\n",
+    "                    indices_count += 1\n",
+    "                \n",
+    "                # NDBI: (SWIR - NIR) / (SWIR + NIR)\n",
+    "                if 'b11' in scene_data and 'nir' in scene_data:\n",
+    "                    swir = scene_data['b11'].astype(float)\n",
+    "                    nir = scene_data['nir'].astype(float)\n",
+    "                    ndbi = (swir - nir) / (swir + nir + 1e-8)\n",
+    "                    scene_data['ndbi'] = ndbi.astype(np.float32)\n",
+    "                    indices_count += 1\n",
+    "                \n",
+    "                # NDWI: (NIR - SWIR) / (NIR + SWIR)\n",
+    "                if 'nir' in scene_data and 'b11' in scene_data:\n",
+    "                    nir = scene_data['nir'].astype(float)\n",
+    "                    swir = scene_data['b11'].astype(float)\n",
+    "                    ndwi = (nir - swir) / (nir + swir + 1e-8)\n",
+    "                    scene_data['ndwi'] = ndwi.astype(np.float32)\n",
+    "                    indices_count += 1\n",
+    "                \n",
+    "                # EVI: Enhanced Vegetation Index\n",
+    "                if 'nir' in scene_data and 'red' in scene_data and 'blue' in scene_data:\n",
+    "                    nir = scene_data['nir'].astype(float)\n",
+    "                    red = scene_data['red'].astype(float)\n",
+    "                    blue = scene_data['blue'].astype(float)\n",
+    "                    evi = 2.5 * (nir - red) / (nir + 6*red - 7.5*blue + 1)\n",
+    "                    scene_data['evi'] = evi.astype(np.float32)\n",
+    "                    indices_count += 1\n",
+    "                    \n",
+    "            except Exception as e:\n",
+    "                pass\n",
+    "        \n",
+    "        print(f\"✅ Calculated {indices_count} spectral indices per scene\")\n",
+    "        \n",
+    "        # ===== STACK SCENES ALONG TIME DIMENSION =====\n",
+    "        print(f\"\\n[STACKING] Stacking all {len(all_data_dict)} scenes to create time-series...\")\n",
+    "        \n",
+    "        data_vars = {}\n",
+    "        band_names = list(all_data_dict[0].keys())\n",
+    "        \n",
+    "        for band_name in band_names:\n",
+    "            band_data_list = []\n",
+    "            for scene_idx in sorted(all_data_dict.keys()):\n",
+    "                if band_name in all_data_dict[scene_idx]:\n",
+    "                    band_data_list.append(all_data_dict[scene_idx][band_name])\n",
+    "            \n",
+    "            if band_data_list:\n",
+    "                stacked = np.stack(band_data_list, axis=0)\n",
+    "                data_vars[band_name] = (['time', 'y', 'x'], stacked)\n",
+    "        \n",
+    "        # Create xarray Dataset with time dimension\n",
+    "        first_band_data = list(all_data_dict[0].values())[0]\n",
+    "        y_size, x_size = first_band_data.shape\n",
+    "        \n",
+    "        data = xr.Dataset(\n",
+    "            data_vars,\n",
+    "            coords={\n",
+    "                'time': np.arange(len(all_data_dict)),\n",
+    "                'x': np.arange(x_size),\n",
+    "                'y': np.arange(y_size)\n",
+    "            }\n",
+    "        )\n",
+    "        \n",
+    "        # ===== CALCULATE TEMPORAL FEATURES FOR ACCURACY =====\n",
+    "        print(f\"\\n[TEMPORAL FEATURES] Computing temporal features from time-series...\")\n",
+    "        \n",
+    "        temporal_features_added = 0\n",
+    "        \n",
+    "        # For NDVI: temporal statistics\n",
+    "        if 'ndvi' in data.data_vars:\n",
+    "            ndvi_ts = data['ndvi']\n",
+    "            \n",
+    "            # Min NDVI (vegetation stress indicator)\n",
+    "            data['ndvi_min'] = ndvi_ts.min(dim='time')\n",
+    "            temporal_features_added += 1\n",
+    "            \n",
+    "            # Max NDVI (peak vegetation)\n",
+    "            data['ndvi_max'] = ndvi_ts.max(dim='time')\n",
+    "            temporal_features_added += 1\n",
+    "            \n",
+    "            # Mean NDVI\n",
+    "            data['ndvi_mean'] = ndvi_ts.mean(dim='time')\n",
+    "            temporal_features_added += 1\n",
+    "            \n",
+    "            # NDVI range (variability)\n",
+    "            data['ndvi_range'] = data['ndvi_max'] - data['ndvi_min']\n",
+    "            temporal_features_added += 1\n",
+    "            \n",
+    "            # NDVI std (temporal consistency)\n",
+    "            data['ndvi_std'] = ndvi_ts.std(dim='time')\n",
+    "            temporal_features_added += 1\n",
+    "        \n",
+    "        # For all indices: mean values (aggregate features)\n",
+    "        for band_name in ['ndbi', 'ndwi', 'evi']:\n",
+    "            if band_name in data.data_vars:\n",
+    "                band_ts = data[band_name]\n",
+    "                data[f'{band_name}_mean'] = band_ts.mean(dim='time')\n",
+    "                temporal_features_added += 1\n",
+    "        \n",
+    "        print(f\"✅ Added {temporal_features_added} temporal/aggregate features\")\n",
+    "        \n",
+    "        # ===== SAVE TO CACHE =====\n",
+    "        print(f\"\\n[CACHE] Saving dataset to cache...\")\n",
+    "        try:\n",
+    "            data.to_netcdf(cache_file, engine='netcdf4')\n",
+    "            cache_size = os.path.getsize(cache_file) / (1024**3)\n",
+    "            print(f\"✅ Dataset saved to cache: {cache_file}\")\n",
+    "            print(f\"   Cache size: {cache_size:.2f} GB\")\n",
+    "        except Exception as e:\n",
+    "            print(f\"⚠️  Error saving cache: {e}\")\n",
+    "        \n",
+    "        print(f\"\\n✅ OPTIMAL Dataset with native resolution + temporal features created!\")\n",
+    "        print(f\"   {'='*70}\")\n",
+    "        print(f\"   🎬 Total scenes (time steps): {len(all_data_dict)}\")\n",
+    "        print(f\"   📊 Total bands/variables: {len(data.data_vars)}\")\n",
+    "        print(f\"   🖼️  Spatial size: {x_size} × {y_size} pixels (NATIVE resolution)\")\n",
+    "        print(f\"   📏 Native resolution: 10m (Sentinel-2 L2A)\")\n",
+    "        print(f\"   ⏰ Temporal range: {scene_dates[0].date()} to {scene_dates[-1].date()}\")\n",
+    "        print(f\"   💾 Total dataset size: {notebook_utils.xarray_object_size(data)}\")\n",
+    "        print(f\"   💿 Cached at: {cache_file}\")\n",
+    "        print(f\"   {'='*70}\")\n",
+    "        \n",
+    "        print(f\"\\n   Dataset dimensions:\")\n",
+    "        for dim, size in data.dims.items():\n",
+    "            print(f\"     {dim}: {size}\")\n",
+    "        \n",
+    "        print(f\"\\n   Variables ({len(data.data_vars)}):\")\n",
+    "        spatial_vars = []\n",
+    "        temporal_vars = []\n",
+    "        for var_name in sorted(data.data_vars):\n",
+    "            if len(data[var_name].shape) == 3:\n",
+    "                spatial_vars.append(f\"{var_name} {data[var_name].shape}\")\n",
+    "            else:\n",
+    "                temporal_vars.append(f\"{var_name} {data[var_name].shape}\")\n",
+    "        \n",
+    "        print(f\"   Spatial time-series ({len(spatial_vars)}):\")\n",
+    "        for v in spatial_vars:\n",
+    "            print(f\"     - {v}\")\n",
+    "        print(f\"   Temporal aggregates ({len(temporal_vars)}):\")\n",
+    "        for v in temporal_vars:\n",
+    "            print(f\"     - {v}\")\n",
+    "        \n",
+    "        print(f\"   {'='*70}\")\n",
+    "        \n",
+    "        display(data)\n",
+    "    \n",
+    "    # ===== EXTRACT NDVI FOR TRAINING =====\n",
+    "    print(f\"\\n[NDVI EXTRACTION] Extracting NDVI for model training...\")\n",
+    "    if 'ndvi_mean' in data.data_vars:\n",
+    "        # Use mean NDVI across time\n",
+    "        ndvi = data['ndvi_mean']\n",
+    "        print(f\"✅ NDVI extracted (mean across time)\")\n",
+    "        print(f\"   Shape: {ndvi.shape}\")\n",
+    "    elif 'ndvi' in data.data_vars:\n",
+    "        # Use first time step if mean not available\n",
+    "        ndvi = data['ndvi'].isel(time=0)\n",
+    "        print(f\"✅ NDVI extracted (first time step)\")\n",
+    "        print(f\"   Shape: {ndvi.shape}\")\n",
+    "    else:\n",
+    "        print(f\"❌ NDVI not found in dataset\")\n",
+    "        ndvi = None\n",
+    "    \n",
+    "except Exception as e:\n",
+    "    print(f\"❌ Error: {e}\")\n",
+    "    import traceback\n",
+    "    traceback.print_exc()\n",
+    "    data = None\n",
+    "    ndvi = None\n",
+    "\n",
+    "print(\"=\"*70)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d2585562-88aa-4c7d-bf70-1f6affcf65d4",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "======================================================================\n",
+      "TRAINING DATA SETUP\n",
+      "======================================================================\n",
+      "\n",
+      "[1] Loading training data: train/ST_training data_updated_1130points_new.shp\n",
+      "    ❌ Error: name 'load_train_data' is not defined\n",
+      "\n",
+      "[2] Label mapping:\n",
+      "    0: Lua tom\n",
+      "    1: Lua\n",
+      "    2: CHN\n",
+      "    3: CLN\n",
+      "    4: TS\n",
+      "    5: Song\n",
+      "    6: Dat xay dung\n",
+      "    7: Rung\n",
+      "\n",
+      "======================================================================\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 🎯 LOAD TRAINING DATA & EXTRACT FEATURES\n",
+    "print(\"=\"*70)\n",
+    "print(\"TRAINING DATA SETUP\")\n",
+    "print(\"=\"*70)\n",
+    "\n",
+    "# Load training points\n",
+    "train_path = \"train/ST_training data_updated_1130points_new.shp\"\n",
+    "print(f\"\\n[1] Loading training data: {train_path}\")\n",
+    "\n",
+    "try:\n",
+    "    train = load_train_data(train_path)\n",
+    "    print(f\"    ✅ Loaded {len(train)} training points\")\n",
+    "    print(f\"    Columns: {list(train.columns)}\")\n",
+    "    train.head()\n",
+    "except Exception as e:\n",
+    "    print(f\"    ❌ Error: {e}\")\n",
+    "    train = None\n",
+    "\n",
+    "# Label mapping\n",
+    "label_mapping = {\n",
+    "    \"Lua tom\": \"0\",\n",
+    "    \"Lua\": \"1\",\n",
+    "    \"CHN\": \"2\",\n",
+    "    \"CLN\": \"3\",\n",
+    "    \"TS\": \"4\",\n",
+    "    \"Song\": \"5\",\n",
+    "    \"Dat xay dung\": \"6\",\n",
+    "    \"Rung\": \"7\",\n",
+    "}\n",
+    "\n",
+    "print(f\"\\n[2] Label mapping:\")\n",
+    "for label, code in label_mapping.items():\n",
+    "    print(f\"    {code}: {label}\")\n",
+    "\n",
+    "print(\"\\n\" + \"=\"*70)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "2e955884-d4af-422d-a8e6-d436199540e0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "======================================================================\n",
+      "MODEL TRAINING\n",
+      "======================================================================\n",
+      "❌ Missing training data or NDVI\n",
+      "======================================================================\n",
+      "CPU times: user 700 μs, sys: 0 ns, total: 700 μs\n",
+      "Wall time: 638 μs\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "# 🤖 RANDOM FOREST MODEL TRAINING\n",
+    "print(\"=\"*70)\n",
+    "print(\"MODEL TRAINING\")\n",
+    "print(\"=\"*70)\n",
+    "\n",
+    "if train is not None and ndvi is not None:\n",
+    "    print(\"\\n[1] Extracting features from NDVI...\")\n",
+    "    try:\n",
+    "        # Extract NDVI values at training point locations\n",
+    "        X = []\n",
+    "        y = []\n",
+    "        \n",
+    "        for idx, point in train.iterrows():\n",
+    "            try:\n",
+    "                # Get NDVI value at point location (nearest neighbor)\n",
+    "                ndvi_val = float(ndvi.sel(x=point.geometry.x, y=point.geometry.y, method='nearest').values)\n",
+    "                label = label_mapping[point.Hientrang]\n",
+    "                \n",
+    "                X.append([ndvi_val])\n",
+    "                y.append(int(label))\n",
+    "            except Exception as e:\n",
+    "                print(f\"    ⚠️  Point {idx}: {e}\")\n",
+    "        \n",
+    "        if len(X) > 0:\n",
+    "            X = np.array(X)\n",
+    "            y = np.array(y)\n",
+    "            print(f\"    ✅ Extracted {len(X)} samples\")\n",
+    "            \n",
+    "            # Split data\n",
+    "            print(f\"\\n[2] Splitting data (80-20)...\")\n",
+    "            from sklearn.model_selection import train_test_split\n",
+    "            X_train, X_test, y_train, y_test = train_test_split(\n",
+    "                X, y, test_size=0.2, random_state=42\n",
+    "            )\n",
+    "            print(f\"    Train: {len(X_train)}, Test: {len(X_test)}\")\n",
+    "            \n",
+    "            # Train model\n",
+    "            print(f\"\\n[3] Training Random Forest...\")\n",
+    "            from sklearn.ensemble import RandomForestClassifier\n",
+    "            from sklearn.metrics import accuracy_score\n",
+    "            \n",
+    "            model = RandomForestClassifier(n_estimators=100, random_state=42, n_jobs=-1)\n",
+    "            model.fit(X_train, y_train)\n",
+    "            \n",
+    "            # Evaluate\n",
+    "            y_pred = model.predict(X_test)\n",
+    "            accuracy = accuracy_score(y_test, y_pred)\n",
+    "            print(f\"    ✅ Model trained!\")\n",
+    "            print(f\"    Accuracy: {accuracy*100:.2f}%\")\n",
+    "            \n",
+    "        else:\n",
+    "            print(f\"    ❌ No samples extracted\")\n",
+    "            model = None\n",
+    "            \n",
+    "    except Exception as e:\n",
+    "        print(f\"    ❌ Error: {e}\")\n",
+    "        import traceback\n",
+    "        traceback.print_exc()\n",
+    "        model = None\n",
+    "else:\n",
+    "    print(\"❌ Missing training data or NDVI\")\n",
+    "    model = None\n",
+    "\n",
+    "print(\"=\"*70)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "f1a14379-ed6e-4897-9ca4-2669743fab40",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "======================================================================\n",
+      "MODEL SAVING\n",
+      "======================================================================\n",
+      "❌ No model to save\n",
+      "======================================================================\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 💾 SAVE MODEL\n",
+    "print(\"=\"*70)\n",
+    "print(\"MODEL SAVING\")\n",
+    "print(\"=\"*70)\n",
+    "\n",
+    "if model is not None:\n",
+    "    print(\"\\n🔄 Saving trained model...\")\n",
+    "    try:\n",
+    "        save_model(\"model_rasterio.joblib\", model)\n",
+    "        print(\"✅ Model saved to model_train/model_rasterio.joblib\")\n",
+    "    except Exception as e:\n",
+    "        print(f\"❌ Error saving model: {e}\")\n",
+    "else:\n",
+    "    print(\"❌ No model to save\")\n",
+    "\n",
+    "print(\"=\"*70)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "33dd516d-9824-499e-96b9-5cd9224c194c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "======================================================================\n",
+      "CLEANUP\n",
+      "======================================================================\n",
+      "\n",
+      "🔄 Closing Dask client and cluster...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Cleanup complete\n",
+      "\n",
+      "======================================================================\n",
+      "✅ PIPELINE COMPLETE\n",
+      "======================================================================\n"
+     ]
+    },
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n",
+      "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n",
+      "\u001b[1;31mClick <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. \n",
+      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
+     ]
+    }
+   ],
+   "source": [
+    "# 🛑 CLEANUP\n",
+    "print(\"=\"*70)\n",
+    "print(\"CLEANUP\")\n",
+    "print(\"=\"*70)\n",
+    "\n",
+    "print(\"\\n🔄 Closing Dask client and cluster...\")\n",
+    "try:\n",
+    "    client.close()\n",
+    "    cluster.close()\n",
+    "    print(\"✅ Cleanup complete\")\n",
+    "except Exception as e:\n",
+    "    print(f\"⚠️  Error during cleanup: {e}\")\n",
+    "\n",
+    "print(\"\\n\" + \"=\"*70)\n",
+    "print(\"✅ PIPELINE COMPLETE\")\n",
+    "print(\"=\"*70)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "env_01",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

CLOUD_PROCESSING.md

@@ -1,346 +0,0 @@
-# Xử lý mây (Cloud Processing) — Hệ thống Land Classification
-
-Tài liệu chi tiết về các phương pháp xử lý mây cho dữ liệu Sentinel-2. Module độc lập `cloud_removal.py` cung cấp nhiều chiến lược có thể chọn.
-
-## Tổng quan
-
-Hệ thống cung cấp **7 phương pháp xử lý mây** khác nhau, từ cổ điển đến hiện đại (ML/DL):
-
-1. **Classic** - 3 bước cổ điển (temporal → median → spatial) - mặc định
-2. **Temporal Only** - Chỉ temporal interpolation (nhanh nhất)
-3. **Median Composite** - Ưu tiên median composite (giảm nhiễu tốt nhất)
-4. **ML KNN** - Machine Learning K-Nearest Neighbors inpainting
-5. **ML RF** - Machine Learning Random Forest inpainting
-6. **Deep Inpainting** - Deep Learning CNN inpainting (yêu cầu model)
-7. **Hybrid** - Kết hợp classical + ML (cân bằng tốc độ và chất lượng)
-
----
-
-## Cách sử dụng
-
-### API Endpoint
-
-Lấy danh sách các methods:
-```bash
-GET /api/cloud-removal/methods
-```
-
-Response:
-```json
-{
-  "success": true,
-  "methods": {
-    "classic": "3-step classical: temporal → median → spatial (default, balanced)",
-    "temporal_only": "Temporal interpolation only (fastest, needs many scenes)",
-    "median_composite": "Median composite priority (best noise reduction)",
-    "ml_knn": "ML K-Nearest Neighbors inpainting (good quality, medium speed)",
-    "ml_rf": "ML Random Forest inpainting (high quality, slower)",
-    "deep": "Deep Learning CNN inpainting (best quality, requires model)",
-    "hybrid": "Hybrid classical + ML (balanced speed & quality)"
-  },
-  "default": "classic"
-}
-```
-
-### Config trong Prediction
-
-Thêm `cloud_removal_method` vào config:
-
-```python
-config = {
-    "model_filename": "model_odc.joblib",
-    "min_lon": 105.5,
-    "max_lon": 105.6,
-    "min_lat": 10.0,
-    "max_lat": 10.1,
-    "start_date": "2024-01-01",
-    "end_date": "2024-12-31",
-    "max_scenes": 12,
-    "cloud_cover": 30,
-    "resolution": 20,
-    "use_gpu": false,
-    "cloud_removal_method": "hybrid"  # Chọn method tại đây
-}
-```
-
-### Programmatic Usage
-
-```python
-from cloud_removal import process_cloud_removal
-
-# Load Sentinel-2 data with SCL band
-s2_data = load(...) 
-
-# Process clouds with selected method
-cleaned_data, metadata = process_cloud_removal(
-    s2_data=s2_data,
-    method="hybrid",  # or "classic", "ml_knn", etc.
-    verbose=True
-)
-
-print(f"Cloud coverage: {metadata['cloud_coverage_percent']:.1f}%")
-print(f"Steps applied: {metadata['steps_applied']}")
-```
-
----
-
-## Chi tiết các phương pháp
-
-### 1. Classic (Mặc định)
-
-**Mô tả:** 3 bước cổ điển kết hợp temporal, median, và spatial interpolation.
-
-**Quy trình:**
-1. Temporal interpolation (ffill + bfill)
-2. Median compositing (nếu >= 3 scenes)
-3. Spatial interpolation (nearest neighbor)
-4. Fallback fillna(0)
-
-**Ưu điểm:**
-- Cân bằng tốc độ và chất lượng
-- Đã được test kỹ, ổn định
-- Phù hợp hầu hết trường hợp
-
-**Nhược điểm:**
-- Không tối ưu cho các gaps lớn
-- Có thể tạo artifacts ở biên
-
-**Khi nào dùng:** Default choice, phù hợp cho production
-
----
-
-### 2. Temporal Only
-
-**Mô tả:** Chỉ sử dụng temporal interpolation (ffill + bfill).
-
-**Ưu điểm:**
-- Nhanh nhất
-- Giữ xu hướng thời gian tốt
-- Ít tạo artifacts
-
-**Nhược điểm:**
-- Yêu cầu nhiều time steps
-- Không xử lý được gaps liên tục
-- Chất lượng kém nếu ít scenes
-
-**Khi nào dùng:** Khi có nhiều scenes (>10) và cần tốc độ
-
----
-
-### 3. Median Composite
-
-**Mô tả:** Ưu tiên median composite, sau đó spatial interpolation.
-
-**Ưu điểm:**
-- Giảm nhiễu tốt nhất
-- Chống outliers hiệu quả
-- Tạo composite trơn
-
-**Nhược điểm:**
-- Mất thông tin temporal
-- Yêu cầu >= 3 scenes
-- Chậm hơn temporal only
-
-**Khi nào dùng:** Khi cần giảm nhiễu, không quan tâm temporal dynamics
-
----
-
-### 4. ML KNN Inpainting
-
-**Mô tả:** Sử dụng K-Nearest Neighbors để học từ pixels hợp lệ và dự đoán pixels bị mây.
-
-**Quy trình:**
-1. Xác định valid pixels (không có mây)
-2. Train KNN model với spatial coordinates + spectral values
-3. Predict invalid pixels
-4. Fill predictions vào dataset
-
-**Ưu điểm:**
-- Chất lượng cao hơn classical
-- Học spatial patterns
-- Không cần pretrained model
-
-**Nhược điểm:**
-- Chậm hơn classical
-- Yêu cầu đủ valid pixels (>10)
-- Tốn RAM nếu ảnh lớn
-
-**Hyperparameters:**
-- n_neighbors: 5
-- weights: 'distance'
-
-**Khi nào dùng:** Khi cần chất lượng cao và có đủ valid pixels
-
----
-
-### 5. ML Random Forest Inpainting
-
-**Mô tả:** Sử dụng Random Forest để inpainting, tương tự KNN nhưng phức tạp hơn.
-
-**Ưu điểm:**
-- Chất lượng cao nhất trong ML methods
-- Xử lý non-linear patterns tốt
-- Robust với outliers
-
-**Nhược điểm:**
-- Chậm nhất trong ML methods
-- Tốn nhiều RAM
-- Có thể overfit với ít data
-
-**Hyperparameters:**
-- n_estimators: 10
-- max_depth: 10
-- n_jobs: -1 (parallel)
-
-**Khi nào dùng:** Khi cần chất lượng tối đa và không quan tâm tốc độ
-
----
-
-### 6. Deep Inpainting (CNN)
-
-**Mô tả:** Sử dụng CNN autoencoder để reconstruct pixels bị mây.
-
-**Trạng thái:** **Đang phát triển** - yêu cầu pretrained model
-
-**Quy trình (planned):**
-1. Stack bands thành multi-channel image
-2. Tạo binary mask (1=cloud, 0=valid)
-3. Run through CNN autoencoder
-4. Blend predictions với valid pixels
-
-**Ưu điểm (khi có model):**
-- Chất lượng tốt nhất
-- Xử lý large gaps hiệu quả
-- Học global context
-
-**Nhược điểm:**
-- Yêu cầu pretrained model
-- Chậm nhất (GPU recommended)
-- Phức tạp để deploy
-
-**Khi nào dùng:** Khi có GPU và pretrained model, cần chất lượng tối đa
-
----
-
-### 7. Hybrid (Khuyến nghị)
-
-**Mô tả:** Kết hợp classical + ML để cân bằng tốc độ và chất lượng.
-
-**Quy trình:**
-1. Temporal interpolation (nhanh)
-2. Check remaining NaN percentage
-3. Nếu > 5%: Apply ML KNN inpainting
-4. Nếu <= 5%: Apply spatial interpolation
-5. Fallback fillna(0)
-
-**Ưu điểm:**
-- Cân bằng tốc độ và chất lượng
-- Adaptive - chỉ dùng ML khi cần
-- Hiệu quả với mọi cloud coverage
-
-**Nhược điểm:**
-- Phức tạp hơn classic
-- Khó debug
-
-**Khi nào dùng:** **Khuyến nghị cho production** - tự động chọn strategy phù hợp
-
----
-
-## So sánh Performance
-
-| Method | Tốc độ | Chất lượng | RAM | Yêu cầu |
-|--------|--------|------------|-----|---------|
-| classic | ⭐⭐⭐⭐ | ⭐⭐⭐ | Thấp | Không |
-| temporal_only | ⭐⭐⭐⭐⭐ | ⭐⭐ | Thấp | Nhiều scenes |
-| median_composite | ⭐⭐⭐ | ⭐⭐⭐⭐ | Thấp | >= 3 scenes |
-| ml_knn | ⭐⭐ | ⭐⭐⭐⭐ | Trung bình | Đủ valid pixels |
-| ml_rf | ⭐ | ⭐⭐⭐⭐⭐ | Cao | Đủ valid pixels |
-| deep | ⭐ | ⭐⭐⭐⭐⭐ | Rất cao | Pretrained model + GPU |
-| hybrid | ⭐⭐⭐ | ⭐⭐⭐⭐ | Trung bình | Không |
-
----
-
-## Phát hiện mây (SCL)
-
-Tất cả methods đều sử dụng SCL (Scene Classification Layer):
-
-```python
-# SCL values:
-# 0: No data, 1: Saturated/Defective, 2: Dark Area Pixels
-# 3: Cloud shadows, 4: Vegetation, 5: Not vegetated, 6: Water
-# 7: Unclassified, 8: Cloud medium probability, 9: Cloud high probability
-# 10: Thin cirrus, 11: Snow/Ice
-
-cloud_mask = (scl == 3) | (scl == 8) | (scl == 9) | (scl == 10) | (scl == 11)
-invalid_mask = (scl == 0) | (scl == 1)
-full_mask = cloud_mask | invalid_mask
-```
-
----
-
-## Testing & Comparison
-
-So sánh nhiều methods trên cùng dữ liệu:
-
-```python
-from cloud_removal import compare_methods
-
-results = compare_methods(
-    s2_data=s2_data,
-    methods=["classic", "temporal_only", "ml_knn", "hybrid"]
-)
-
-for method, result in results.items():
-    print(f"{method}: {result['remaining_nan_percent']:.2f}% NaN remaining")
-```
-
----
-
-## Khuyến nghị sử dụng
-
-### Production (General)
-```
-cloud_removal_method: "hybrid"
-```
-- Cân bằng tốc độ và chất lượng
-- Adaptive theo cloud coverage
-
-### High Quality (Research)
-```
-cloud_removal_method: "ml_rf"
-```
-- Chất lượng tối đa
-- Chấp nhận tốc độ chậm
-
-### Fast Processing (Monitoring)
-```
-cloud_removal_method: "temporal_only"
-```
-- Cần nhiều scenes (>10)
-- Ưu tiên tốc độ
-
-### Low Cloud Coverage (<10%)
-```
-cloud_removal_method: "classic"
-```
-- Đơn giản, hiệu quả
-- Ổn định, đã test kỹ
-
----
-
-## Vị trí code
-
-- **Module:** `cloud_removal.py` - Standalone cloud removal module
-- **API Integration:** `api_server.py` - API endpoints và config
-- **Documentation:** `CLOUD_PROCESSING.md` - Tài liệu này
-
----
-
-## Phát triển tiếp
-
-- [ ] Implement CNN autoencoder cho deep inpainting
-- [ ] Add quality scoring system
-- [ ] Optimize ML methods với Dask
-- [ ] Add weighted temporal interpolation
-- [ ] Support custom ML models
-

CLOUD_REMOVAL_UPLOAD_GUIDE.md

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

CLOUD_TRAINING_GUIDE.md

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

COLAB_FRP_DEMO_GUIDE.md

@@ -0,0 +1,445 @@
+# Demo Website trên Google Colab với FRP
+
+Hướng dẫn này giúp bạn chạy toàn bộ hệ thống **remote-sensing** trên Google Colab  
+và expose ra internet qua **FRP (Fast Reverse Proxy)** — không cần ngrok, URL cố định, không giới hạn session.
+
+---
+
+## Yêu cầu
+
+| Thành phần | Mô tả |
+|---|---|
+| **Google Colab** | Tài khoản Google thông thường (free tier là đủ) |
+| **Google Drive** | Dùng để lưu project và models |
+| **1 VPS có IP public** | Chạy `frps` server — VPS $3–5/tháng là đủ |
+| **Port mở trên VPS** | 7000 (FRP control) + 8080 (web traffic) |
+
+---
+
+## Phần 1 — Chuẩn bị VPS (chạy 1 lần, giữ mãi)
+
+### Bước 1.1 — Download FRP lên VPS
+
+SSH vào VPS rồi chạy:
+
+```bash
+wget https://github.com/fatedier/frp/releases/download/v0.61.1/frp_0.61.1_linux_amd64.tar.gz
+tar -xzf frp_0.61.1_linux_amd64.tar.gz
+cd frp_0.61.1_linux_amd64
+```
+
+### Bước 1.2 — Tạo file cấu hình `frps.toml`
+
+```bash
+cat > frps.toml << 'EOF'
+bindPort = 7000
+auth.token = "your_secret_token_here"
+
+# Dashboard để theo dõi kết nối (tuỳ chọn)
+webServer.port = 7500
+webServer.user = "admin"
+webServer.password = "admin123"
+EOF
+```
+
+> ⚠️ Đặt `auth.token` thành chuỗi bí mật của bạn, ví dụ: `"rs_demo_2026_abc123"`. Phải giống với phía Colab.
+
+### Bước 1.3 — Chạy frps
+
+```bash
+# Test chạy foreground (Ctrl+C để dừng)
+./frps -c frps.toml
+
+# Chạy nền (production)
+nohup ./frps -c frps.toml > frps.log 2>&1 &
+
+# Kiểm tra đang chạy
+ps aux | grep frps
+```
+
+### Bước 1.4 — Mở firewall VPS
+
+```bash
+# Ubuntu/Debian
+ufw allow 7000    # FRP control port
+ufw allow 8080    # Web traffic port
+ufw allow 7500    # Dashboard (tuỳ chọn)
+ufw reload
+
+# CentOS/RHEL
+firewall-cmd --permanent --add-port=7000/tcp
+firewall-cmd --permanent --add-port=8080/tcp
+firewall-cmd --reload
+```
+
+### Bước 1.5 — Kiểm tra frps hoạt động
+
+```bash
+# Xem log
+tail -f frps.log
+
+# Kết quả kỳ vọng:
+# [frps] frp service started, listen on 0.0.0.0:7000
+```
+
+---
+
+## Phần 2 — Chuẩn bị Google Drive
+
+### Bước 2.1 — Upload project lên Drive
+
+Cấu trúc thư mục trên Google Drive:
+
+```
+My Drive/
+└── remote-sensing/
+    ├── api_server.py
+    ├── train_module.py
+    ├── feature_extractor.py
+    ├── model_manager.py
+    ├── cloud_removal.py
+    ├── report_generator.py
+    ├── generate_previews.py
+    ├── vietnam_provinces.py
+    ├── vietnam_provinces_merged.py
+    ├── utils.py
+    ├── model_train/            ← copy toàn bộ models đã train
+    │   ├── *.joblib
+    │   └── *.json
+    ├── cloud_removal_model/    ← copy nếu dùng cloud removal DL
+    │   └── *.pth
+    ├── predictions/            ← để trống, Colab sẽ tạo output vào đây
+    ├── reports/                ← để trống
+    └── training_interface.html ← và tất cả *.html
+```
+
+> 💡 Upload nhanh nhất: zip toàn bộ folder `remote-sensing`, upload 1 file zip lên Drive, rồi giải nén bằng Colab.
+
+---
+
+## Phần 3 — Notebook Google Colab
+
+Tạo notebook mới tại [colab.google.com](https://colab.google.com) và paste từng cell sau.
+
+---
+
+### Cell 1 — Mount Drive và di chuyển vào project
+
+```python
+from google.colab import drive
+drive.mount('/content/drive')
+
+import os
+
+PROJECT_PATH = '/content/drive/MyDrive/remote-sensing'
+os.chdir(PROJECT_PATH)
+
+print(f"Working directory: {os.getcwd()}")
+print("Files:", os.listdir()[:10])
+```
+
+---
+
+### Cell 2 — Giải nén nếu upload dạng zip (tuỳ chọn)
+
+```python
+# Chỉ chạy nếu bạn upload file zip
+import zipfile
+
+ZIP_PATH = '/content/drive/MyDrive/remote-sensing.zip'
+EXTRACT_TO = '/content/drive/MyDrive/'
+
+if os.path.exists(ZIP_PATH):
+    with zipfile.ZipFile(ZIP_PATH, 'r') as z:
+        z.extractall(EXTRACT_TO)
+    print("✅ Extracted successfully")
+else:
+    print("⏭️  No zip found, skipping")
+```
+
+---
+
+### Cell 3 — Cài dependencies (chạy lần đầu, ~8–12 phút)
+
+```python
+print("Installing core API dependencies...")
+!pip install -q fastapi uvicorn pydantic
+
+print("Installing geospatial + ML dependencies...")
+!pip install -q \
+    numpy pandas xarray rasterio rioxarray geopandas shapely \
+    scikit-learn xgboost joblib \
+    matplotlib pillow markdown
+
+print("Installing Planetary Computer dependencies...")
+!pip install -q pystac-client planetary-computer odc-stac
+
+print("Installing PyTorch (GPU)...")
+!pip install -q torch torchvision \
+    --extra-index-url https://download.pytorch.org/whl/cu118
+
+print("✅ All dependencies installed")
+```
+
+---
+
+### Cell 4 — Kiểm tra GPU và môi trường
+
+```python
+import torch
+
+print(f"PyTorch version : {torch.__version__}")
+print(f"GPU available   : {torch.cuda.is_available()}")
+if torch.cuda.is_available():
+    print(f"GPU name        : {torch.cuda.get_device_name(0)}")
+
+import rasterio, xarray, geopandas
+print(f"rasterio        : {rasterio.__version__}")
+print(f"xarray          : {xarray.__version__}")
+print(f"geopandas       : {geopandas.__version__}")
+```
+
+---
+
+### Cell 5 — Download và cấu hình frpc
+
+```python
+import subprocess, os
+
+# Download frpc
+!wget -q https://github.com/fatedier/frp/releases/download/v0.61.1/frp_0.61.1_linux_amd64.tar.gz \
+    -O /tmp/frp.tar.gz
+!tar -xzf /tmp/frp.tar.gz -C /tmp/
+!chmod +x /tmp/frp_0.61.1_linux_amd64/frpc
+
+# ============================================================
+# ⚠️  SỬA 2 DÒNG NÀY TRƯỚC KHI CHẠY
+VPS_IP    = "123.456.789.000"          # IP public của VPS bạn
+FRP_TOKEN = "your_secret_token_here"   # Phải giống frps.toml trên VPS
+# ============================================================
+
+frpc_config = f"""
+serverAddr = "{VPS_IP}"
+serverPort = 7000
+auth.token = "{FRP_TOKEN}"
+
+[[proxies]]
+name = "remote-sensing-web"
+type = "tcp"
+localIP = "127.0.0.1"
+localPort = 8000
+remotePort = 8080
+"""
+
+with open('/tmp/frpc.toml', 'w') as f:
+    f.write(frpc_config)
+
+print(f"✅ frpc configured → VPS: {VPS_IP}:8080")
+```
+
+---
+
+### Cell 6 — Khởi động FastAPI server
+
+```python
+import subprocess, time, os
+
+os.chdir('/content/drive/MyDrive/remote-sensing')
+
+# Khởi động FastAPI
+server = subprocess.Popen(
+    ["uvicorn", "api_server:app",
+     "--host", "127.0.0.1",
+     "--port", "8000",
+     "--log-level", "warning"],
+    stdout=subprocess.PIPE,
+    stderr=subprocess.PIPE
+)
+
+time.sleep(4)
+
+# Kiểm tra server đã lên chưa
+if server.poll() is None:
+    print("✅ FastAPI server is running on port 8000")
+else:
+    out, err = server.communicate()
+    print("❌ Server failed to start:")
+    print(err.decode()[:1000])
+```
+
+---
+
+### Cell 7 — Khởi động frpc tunnel
+
+```python
+import subprocess, time
+
+frpc = subprocess.Popen(
+    ["/tmp/frp_0.61.1_linux_amd64/frpc", "-c", "/tmp/frpc.toml"],
+    stdout=subprocess.PIPE,
+    stderr=subprocess.PIPE
+)
+
+time.sleep(3)
+
+if frpc.poll() is None:
+    print("✅ FRP tunnel is active")
+    print("=" * 55)
+    print(f"🌐  Main site    : http://{VPS_IP}:8080")
+    print(f"📊  Dashboard    : http://{VPS_IP}:8080/dashboard")
+    print(f"🏋️   Training     : http://{VPS_IP}:8080/training")
+    print(f"🔮  Prediction   : http://{VPS_IP}:8080/prediction")
+    print(f"📦  Batch        : http://{VPS_IP}:8080/batch")
+    print(f"🌿  NDVI         : http://{VPS_IP}:8080/ndvi")
+    print(f"📈  Reports      : http://{VPS_IP}:8080/reports")
+    print("=" * 55)
+else:
+    out, err = frpc.communicate()
+    print("❌ FRP failed:")
+    print(err.decode()[:500])
+```
+
+---
+
+### Cell 8 — Kiểm tra toàn bộ hệ thống
+
+```python
+import urllib.request, json
+
+BASE = "http://127.0.0.1:8000"
+checks = [
+    ("/api/network/check",     "Network connectivity"),
+    ("/api/models/list",       "Model list"),
+    ("/api/provinces/list",    "Province list"),
+    ("/api/cloud-removal/methods", "Cloud removal methods"),
+]
+
+for path, label in checks:
+    try:
+        r = urllib.request.urlopen(BASE + path, timeout=5)
+        data = json.loads(r.read())
+        status = "✅"
+    except Exception as e:
+        data = str(e)
+        status = "❌"
+    print(f"{status}  {label:<30} → {str(data)[:80]}")
+```
+
+---
+
+### Cell 9 — Xem log nếu có lỗi (tuỳ chọn)
+
+```python
+# Xem stderr của FastAPI
+import select, sys
+
+if server.poll() is not None:
+    _, err = server.communicate()
+    print("FastAPI stderr:")
+    print(err.decode())
+else:
+    # Đọc log không block
+    import os
+    flags = os.O_RDONLY | os.O_NONBLOCK
+    try:
+        fd = server.stderr.fileno()
+        os.set_blocking(fd, False)
+        print(server.stderr.read(2000).decode())
+    except:
+        print("Server is running (no errors captured)")
+```
+
+---
+
+### Cell 10 — Dừng server khi xong demo
+
+```python
+import subprocess
+
+server.terminate()
+frpc.terminate()
+
+print("✅ FastAPI stopped")
+print("✅ FRP tunnel closed")
+```
+
+---
+
+## Phần 4 — Dùng domain thay vì IP (nâng cao)
+
+Nếu VPS có domain riêng, bạn có thể truy cập bằng URL đẹp hơn.
+
+### Sửa `frps.toml` trên VPS
+
+```toml
+bindPort = 7000
+auth.token = "your_secret_token_here"
+vhostHTTPPort = 80
+```
+
+### Sửa Cell 5 (`frpc.toml`) trên Colab
+
+```toml
+serverAddr = "your-vps.com"
+serverPort = 7000
+auth.token = "your_secret_token_here"
+
+[[proxies]]
+name = "remote-sensing-web"
+type = "http"
+localPort = 8000
+customDomains = ["demo.your-vps.com"]
+```
+
+### DNS — trỏ subdomain về VPS
+
+```
+demo.your-vps.com  →  A record  →  123.456.789.000
+```
+
+→ Truy cập: `http://demo.your-vps.com` (không cần `:8080`)
+
+---
+
+## Phần 5 — Giữ session Colab sống lâu hơn
+
+Colab tự disconnect sau ~90 phút idle. Để tránh:
+
+```javascript
+// Paste vào console trình duyệt (F12 → Console)
+function keepAlive() {
+    document.querySelector('#connect button')?.click();
+    console.log('keep-alive ping:', new Date().toLocaleTimeString());
+}
+setInterval(keepAlive, 60000);
+```
+
+Hoặc dùng **Colab Pro** ($10/tháng) để không bị giới hạn session.
+
+---
+
+## Phần 6 — Tóm tắt so sánh FRP vs ngrok
+
+| Tiêu chí | FRP (self-host) | ngrok (free) |
+|---|---|---|
+| URL cố định | ✅ Có | ❌ Đổi mỗi session |
+| Session timeout | ✅ Không giới hạn | ⚠️ 2 giờ |
+| Chi phí | Free (cần VPS) | Free tier có giới hạn |
+| Dữ liệu qua server bên thứ 3 | ❌ Không | ✅ Qua server ngrok |
+| Cần setup | ⚠️ Cần cài frps trên VPS | ✅ Chạy ngay |
+| Phù hợp | Demo dài hạn, production | Demo nhanh 1 lần |
+
+---
+
+## Checklist trước khi demo
+
+- [ ] VPS đang chạy `frps` và port 7000, 8080 đã mở
+- [ ] Project đã upload đầy đủ lên Google Drive (kể cả `model_train/`)
+- [ ] Đã điền đúng `VPS_IP` và `FRP_TOKEN` trong Cell 5
+- [ ] Cell 3 (cài deps) đã chạy thành công
+- [ ] Cell 8 (health check) cho thấy tất cả ✅
+- [ ] Truy cập `http://VPS_IP:8080/dashboard` từ trình duyệt → hiện trang
+
+---
+
+*Tạo ngày: 03/04/2026 — dự án remote-sensing Vietnam Land Classification*

IMPLEMENTATION_SUMMARY.md

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