remote-sensing/01.train_ODC.ipynb

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       "  var render_items = [{\"docid\":\"a316ca14-7fe0-43dd-a90e-dcd56eec7eb2\",\"roots\":{\"p1004\":\"b79801db-b3e0-4fac-8655-6bf348351efb\"},\"root_ids\":[\"p1004\"]}];\n",
       "  var docs = Object.values(docs_json)\n",
       "  if (!docs) {\n",
       "    return\n",
       "  }\n",
       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
       "  async function embed_document(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    await Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "    for (const render_item of render_items) {\n",
       "      for (const root_id of render_item.root_ids) {\n",
       "\tconst id_el = document.getElementById(root_id)\n",
       "\tif (id_el.children.length && id_el.children[0].hasAttribute('data-root-id')) {\n",
       "\t  const root_el = id_el.children[0]\n",
       "\t  root_el.id = root_el.id + '-rendered'\n",
       "\t  for (const child of root_el.children) {\n",
       "            // Ensure JupyterLab does not capture keyboard shortcuts\n",
       "            // see: https://jupyterlab.readthedocs.io/en/4.1.x/extension/notebook.html#keyboard-interaction-model\n",
       "\t    child.setAttribute('data-lm-suppress-shortcuts', 'true')\n",
       "\t  }\n",
       "\t}\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "  function get_bokeh(root) {\n",
       "    if (root.Bokeh === undefined) {\n",
       "      return null\n",
       "    } else if (root.Bokeh.version !== py_version) {\n",
       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
       "\treturn null\n",
       "      }\n",
       "      return root.Bokeh.versions.get(py_version);\n",
       "    } else if (root.Bokeh.version === py_version) {\n",
       "      return root.Bokeh\n",
       "    }\n",
       "    return null\n",
       "  }\n",
       "  function is_loaded(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    return (Bokeh != null && Bokeh.Panel !== undefined)\n",
       "  }\n",
       "  if (is_loaded(root)) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (is_loaded(root)) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else if (document.readyState == \"complete\") {\n",
       "        attempts++;\n",
       "        if (attempts > 200) {\n",
       "          clearInterval(timer);\n",
       "\t  var Bokeh = get_bokeh(root)\n",
       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
       "\t  } else {\n",
       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
       "\t    embed_document(root)\n",
       "\t  }\n",
       "        }\n",
       "      }\n",
       "    }, 25, root)\n",
       "  }\n",
       "})(window);</script>"
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       "id": "p1004"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<script type=\"esms-options\">{\"shimMode\": true}</script><style>*[data-root-id],\n",
       "*[data-root-id] > * {\n",
       "  box-sizing: border-box;\n",
       "  font-family: var(--jp-ui-font-family);\n",
       "  font-size: var(--jp-ui-font-size1);\n",
       "  color: var(--vscode-editor-foreground, var(--jp-ui-font-color1));\n",
       "}\n",
       "\n",
       "/* Override VSCode background color */\n",
       ".cell-output-ipywidget-background:has(\n",
       "    > .cell-output-ipywidget-background > .lm-Widget > *[data-root-id]\n",
       "  ),\n",
       ".cell-output-ipywidget-background:has(> .lm-Widget > *[data-root-id]) {\n",
       "  background-color: transparent !important;\n",
       "}\n",
       "</style>"
      ]
     },
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i < links.length; i++) {\n      const link = links[i]\n      if (link.href != null) {\n        existing_stylesheets.push(link.href)\n      }\n    }\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const escaped = encodeURI(url)\n      if (existing_stylesheets.indexOf(escaped) !== -1) {\n        on_load()\n        continue;\n      }\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }    var existing_scripts = []\n    const scripts = document.getElementsByTagName('script')\n    for (let i = 0; i < scripts.length; i++) {\n      var script = scripts[i]\n      if (script.src != null) {\n        existing_scripts.push(script.src)\n      }\n    }\n    for (let i = 0; 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      "application/vnd.holoviews_load.v0+json": ""
     },
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     "output_type": "display_data"
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     "data": {
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      "application/vnd.holoviews_load.v0+json": ""
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ All modules loaded successfully\n",
      "CPU times: user 282 ms, sys: 7.6 ms, total: 290 ms\n",
      "Wall time: 311 ms\n"
     ]
    }
   ],
   "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": 11,
   "id": "d824dc4f-994b-4d1c-8d24-ce6674da141c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ AWS credentials loaded from environment variables\n",
      "✅ Dask cluster initialized\n",
      "   Cluster: LocalCluster(be94629e, 'tcp://127.0.0.1:33823', workers=4, threads=40, memory=128.00 GiB)\n",
      "✅ S3 access configured (requester_pays=True)\n",
      "✅ Datacube connected (metadata only)\n",
      "\n",
      "======================================================================\n",
      "CPU times: user 231 ms, sys: 11.7 ms, total: 242 ms\n",
      "Wall time: 5.68 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",
    "# Cấu hình S3 access cho rasterio/GDAL (bắt buộc để đọc COGs từ S3)\n",
    "configure_s3_access(aws_unsigned=False, requester_pays=True, client=client)\n",
    "print(\"✅ S3 access configured (requester_pays=True)\")\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)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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 180337 scenes\n",
      "\n",
      "[2] Selected scene: S2B_56MNC_20230525_0_L2A\n",
      "    Date: 2023-05-25 00:17:32.223000+00:00\n",
      "\n",
      "[3] Available bands:\n",
      "    - aot: s3://sentinel-cogs/sentinel-s2-l2a-cogs/56/M/NC/2023/5/S2B_56MNC_20230525_0_L2A/\n",
      "    - nir: s3://sentinel-cogs/sentinel-s2-l2a-cogs/56/M/NC/2023/5/S2B_56MNC_20230525_0_L2A/\n",
      "    - red: s3://sentinel-cogs/sentinel-s2-l2a-cogs/56/M/NC/2023/5/S2B_56MNC_20230525_0_L2A/\n",
      "    - scl: s3://sentinel-cogs/sentinel-s2-l2a-cogs/56/M/NC/2023/5/S2B_56MNC_20230525_0_L2A/\n",
      "    - wvp: s3://sentinel-cogs/sentinel-s2-l2a-cogs/56/M/NC/2023/5/S2B_56MNC_20230525_0_L2A/\n",
      "    - blue: s3://sentinel-cogs/sentinel-s2-l2a-cogs/56/M/NC/2023/5/S2B_56MNC_20230525_0_L2A/\n",
      "    - green: s3://sentinel-cogs/sentinel-s2-l2a-cogs/56/M/NC/2023/5/S2B_56MNC_20230525_0_L2A/\n",
      "    - nir08: s3://sentinel-cogs/sentinel-s2-l2a-cogs/56/M/NC/2023/5/S2B_56MNC_20230525_0_L2A/\n",
      "    - nir09: s3://sentinel-cogs/sentinel-s2-l2a-cogs/56/M/NC/2023/5/S2B_56MNC_20230525_0_L2A/\n",
      "    - swir16: s3://sentinel-cogs/sentinel-s2-l2a-cogs/56/M/NC/2023/5/S2B_56MNC_20230525_0_L2A/\n",
      "    - swir22: s3://sentinel-cogs/sentinel-s2-l2a-cogs/56/M/NC/2023/5/S2B_56MNC_20230525_0_L2A/\n",
      "    - coastal: s3://sentinel-cogs/sentinel-s2-l2a-cogs/56/M/NC/2023/5/S2B_56MNC_20230525_0_L2A/\n",
      "    - rededge1: s3://sentinel-cogs/sentinel-s2-l2a-cogs/56/M/NC/2023/5/S2B_56MNC_20230525_0_L2A/\n",
      "    - rededge2: s3://sentinel-cogs/sentinel-s2-l2a-cogs/56/M/NC/2023/5/S2B_56MNC_20230525_0_L2A/\n",
      "    - rededge3: s3://sentinel-cogs/sentinel-s2-l2a-cogs/56/M/NC/2023/5/S2B_56MNC_20230525_0_L2A/\n",
      "======================================================================\n",
      "CPU times: user 1min 32s, sys: 11.1 s, total: 1min 43s\n",
      "Wall time: 5min 20s\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": 13,
   "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 765 μs, sys: 0 ns, total: 765 μs\n",
      "Wall time: 482 μs\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": null,
   "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",
      "✅ S3 access configured for local process\n",
      "\n",
      "📥 Downloading from S3...\n",
      "\n",
      "📦 Found 180337 available scenes\n",
      "   Date range: 2023-03-01 to 2023-12-31\n",
      "\n",
      "[LOADING] Loading 1 scenes with ALL available bands...\n",
      "   (Keeping NATIVE resolution - NO upsampling/magnification)\n",
      "   Available bands: ['aot', 'nir', 'red', 'scl', 'wvp', 'blue', 'green', 'nir08', 'nir09', 'swir16', 'swir22', 'coastal', 'rededge1', 'rededge2', 'rededge3']\n",
      "\n",
      "   [ 1/1] S2B_56MNC_20230525_0_L2A (2023-05-25)\n",
      "      ⚠️ Error loading aot: HTTP response code: 403\n",
      "      ⚠️ Error loading nir: HTTP response code: 403\n",
      "      ⚠️ Error loading red: HTTP response code: 403\n",
      "      ⚠️ Error loading scl: HTTP response code: 403\n",
      "      ⚠️ Error loading wvp: HTTP response code: 403\n",
      "      ⚠️ Error loading blue: HTTP response code: 403\n",
      "      ⚠️ Error loading green: HTTP response code: 403\n",
      "      ⚠️ Error loading nir08: HTTP response code: 403\n",
      "      ⚠️ Error loading nir09: HTTP response code: 403\n",
      "      ⚠️ Error loading swir16: HTTP response code: 403\n",
      "      ⚠️ Error loading swir22: HTTP response code: 403\n",
      "      ⚠️ Error loading coastal: HTTP response code: 403\n",
      "      ⚠️ Error loading rededge1: HTTP response code: 403\n",
      "      ⚠️ Error loading rededge2: HTTP response code: 403\n",
      "      ⚠️ Error loading rededge3: HTTP response code: 403\n",
      "❌ Error: Could not load any bands from any scene\n",
      "======================================================================\n",
      "CPU times: user 1min 37s, sys: 8.69 s, total: 1min 46s\n",
      "Wall time: 2min 32s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Traceback (most recent call last):\n",
      "  File \"<timed exec>\", line 88, in <module>\n",
      "ValueError: Could not load any bands from any scene\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",
    "    # Cấu hình S3 access cho PROCESS CHÍNH (không phải workers)\n",
    "    configure_s3_access(aws_unsigned=False, requester_pays=True)\n",
    "    print(\"✅ S3 access configured for local process\")\n",
    "\n",
    "    # Kiểm tra AWS credentials\n",
    "    aws_key = os.environ.get('AWS_ACCESS_KEY_ID', '')\n",
    "    aws_secret = os.environ.get('AWS_SECRET_ACCESS_KEY', '')\n",
    "    if aws_key and aws_secret:\n",
    "        print(f\"✅ AWS credentials found (key: {aws_key[:8]}...)\")\n",
    "    else:\n",
    "        print(\"⚠️  WARNING: AWS credentials not found in environment!\")\n",
    "        print(\"   Set AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY to fix HTTP 403 errors.\")\n",
    "        print(\"   Trying unsigned access as fallback...\")\n",
    "        configure_s3_access(aws_unsigned=True)\n",
    "        print(\"   Switched to aws_unsigned=True (public buckets only)\")\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 (filtered to Vietnam region)\n",
    "        datasets = list(dc.find_datasets(\n",
    "            product='s2_l2a',\n",
    "            time=date_range,\n",
    "            lat=latitude_range,\n",
    "            lon=longtitude_range\n",
    "        ))\n",
    "        \n",
    "        if not datasets:\n",
    "            raise ValueError(\"No datasets found for date range and region\")\n",
    "        \n",
    "        print(f\"\\n📦 Found {len(datasets)} available scenes\")\n",
    "        print(f\"   Date range: {date_range[0]} to {date_range[1]}\")\n",
    "        print(f\"   Region: lon={longtitude_range}, lat={latitude_range}\")\n",
    "        \n",
    "        # ===== LOAD ALL SCENES WITH ALL AVAILABLE BANDS (NO MAGNIFICATION) =====\n",
    "        # num_scenes = len(datasets)  # Load ALL scenes\n",
    "        num_scenes = 1 # Load ALL scenes\n",
    "        print(f\"\\n[LOADING] Loading {num_scenes} scenes with ALL available bands...\")\n",
    "        print(f\"   (Keeping NATIVE resolution - NO upsampling/magnification)\")\n",
    "        \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)[:50]}\")\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",
    "        # Map band names: notebook dùng 'swir16'/'swir22' thay vì 'b11'/'b12'\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)  — dùng swir16 thay b11\n",
    "                swir_key = 'swir16' if 'swir16' in scene_data else ('b11' if 'b11' in scene_data else None)\n",
    "                if swir_key and 'nir' in scene_data:\n",
    "                    swir = scene_data[swir_key].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 swir_key and 'nir' in scene_data:\n",
    "                    nir = scene_data['nir'].astype(float)\n",
    "                    swir = scene_data[swir_key].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",
    "            os.makedirs(cache_dir, exist_ok=True)\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)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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: train/ST_training data_updated_1130points_new.shp: No such file or directory\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": 16,
   "id": "2e955884-d4af-422d-a8e6-d436199540e0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "======================================================================\n",
      "LAND USE CLASSIFICATION TRAINING\n",
      "======================================================================\n",
      "\n",
      "🎯 Mục tiêu: Dự đoán phân loại sử dụng đất (8 lớp)\n",
      "   - NDVI/NDWI/NDBI/EVI là INPUT FEATURES\n",
      "   - Sau khi predict xong → có thể hiển thị NDVI map như chỉ số phụ\n",
      "======================================================================\n",
      "❌ Missing training data or satellite data\n",
      "\n",
      "======================================================================\n",
      "📝 NOTE: Model này dự đoán PHÂN LOẠI SỬ DỤNG ĐẤT (8 lớp)\n",
      "    NDVI là một trong các features đầu vào, không phải mục tiêu dự đoán\n",
      "    Sau khi predict → có thể hiển thị NDVI map như chỉ số phụ\n",
      "======================================================================\n",
      "CPU times: user 215 μs, sys: 0 ns, total: 215 μs\n",
      "Wall time: 180 μs\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# 🤖 LAND USE CLASSIFICATION MODEL TRAINING (MỤC TIÊU CHÍNH)\n",
    "print(\"=\"*70)\n",
    "print(\"LAND USE CLASSIFICATION TRAINING\")\n",
    "print(\"=\"*70)\n",
    "print(\"\\n🎯 Mục tiêu: Dự đoán phân loại sử dụng đất (8 lớp)\")\n",
    "print(\"   - NDVI/NDWI/NDBI/EVI là INPUT FEATURES\")\n",
    "print(\"   - Sau khi predict xong → có thể hiển thị NDVI map như chỉ số phụ\")\n",
    "print(\"=\"*70)\n",
    "\n",
    "if train is not None and data is not None:\n",
    "    print(\"\\n[1] Extracting MULTIPLE features from satellite data...\")\n",
    "    print(\"    (Sử dụng nhiều spectral indices để cải thiện accuracy)\")\n",
    "    \n",
    "    try:\n",
    "        # Extract features at training point locations\n",
    "        X = []\n",
    "        y = []\n",
    "        \n",
    "        # Available features from data\n",
    "        available_features = ['ndvi_mean', 'ndvi_min', 'ndvi_max', 'ndvi_std', 'ndvi_range',\n",
    "                             'ndwi_mean', 'ndbi_mean', 'evi_mean']\n",
    "        \n",
    "        # Check which features are actually available\n",
    "        features_to_use = [f for f in available_features if f in data.data_vars]\n",
    "        \n",
    "        if not features_to_use:\n",
    "            print(\"    ❌ No spectral features found in dataset!\")\n",
    "            print(\"    Available variables:\", list(data.data_vars))\n",
    "            model = None\n",
    "        else:\n",
    "            print(f\"    Using {len(features_to_use)} features: {features_to_use}\")\n",
    "            \n",
    "            for idx, point in train.iterrows():\n",
    "                try:\n",
    "                    # Extract all available features at this point\n",
    "                    feature_vec = []\n",
    "                    for feat_name in features_to_use:\n",
    "                        feat_val = float(data[feat_name].sel(\n",
    "                            x=point.geometry.x, \n",
    "                            y=point.geometry.y, \n",
    "                            method='nearest'\n",
    "                        ).values)\n",
    "                        feature_vec.append(feat_val)\n",
    "                    \n",
    "                    # Get label\n",
    "                    label = label_mapping[point.Hientrang]\n",
    "                    \n",
    "                    # Only add if no NaN values\n",
    "                    if not np.isnan(feature_vec).any():\n",
    "                        X.append(feature_vec)\n",
    "                        y.append(int(label))\n",
    "                except Exception as e:\n",
    "                    # Skip points with errors\n",
    "                    continue\n",
    "            \n",
    "            if len(X) > 0:\n",
    "                X = np.array(X)\n",
    "                y = np.array(y)\n",
    "                print(f\"    ✅ Extracted {len(X)} samples with {X.shape[1]} features each\")\n",
    "                \n",
    "                # Show feature statistics\n",
    "                print(f\"\\n    Feature statistics:\")\n",
    "                for i, feat_name in enumerate(features_to_use):\n",
    "                    print(f\"      {feat_name:15s}: mean={X[:,i].mean():.3f}, std={X[:,i].std():.3f}\")\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, stratify=y\n",
    "                )\n",
    "                print(f\"    Train: {len(X_train)}, Test: {len(X_test)}\")\n",
    "                \n",
    "                # Show class distribution\n",
    "                unique, counts = np.unique(y_train, return_counts=True)\n",
    "                print(f\"\\n    Class distribution in training set:\")\n",
    "                for cls, count in zip(unique, counts):\n",
    "                    cls_name = [k for k, v in label_mapping.items() if v == str(cls)][0]\n",
    "                    print(f\"      {cls}: {cls_name:15s} - {count:4d} samples ({count/len(y_train)*100:.1f}%)\")\n",
    "                \n",
    "                # Train model\n",
    "                print(f\"\\n[3] Training Random Forest for LAND USE CLASSIFICATION...\")\n",
    "                from sklearn.ensemble import RandomForestClassifier\n",
    "                from sklearn.metrics import accuracy_score, classification_report\n",
    "                \n",
    "                model = RandomForestClassifier(\n",
    "                    n_estimators=200,  # More trees for better accuracy\n",
    "                    max_depth=30,\n",
    "                    min_samples_split=5,\n",
    "                    random_state=42,\n",
    "                    n_jobs=-1,\n",
    "                    verbose=1\n",
    "                )\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",
    "                \n",
    "                print(f\"\\n    ✅ Model trained!\")\n",
    "                print(f\"    Training accuracy: {model.score(X_train, y_train)*100:.2f}%\")\n",
    "                print(f\"    Testing accuracy: {accuracy*100:.2f}%\")\n",
    "                \n",
    "                # Show feature importance\n",
    "                print(f\"\\n    Feature importance:\")\n",
    "                importances = model.feature_importances_\n",
    "                indices = np.argsort(importances)[::-1]\n",
    "                for i, idx in enumerate(indices):\n",
    "                    print(f\"      {i+1}. {features_to_use[idx]:15s}: {importances[idx]:.4f}\")\n",
    "                \n",
    "                # Classification report\n",
    "                print(f\"\\n[4] Classification Report:\")\n",
    "                class_names = [k for k, v in sorted(label_mapping.items(), key=lambda x: x[1])]\n",
    "                print(classification_report(y_test, y_pred, target_names=class_names, zero_division=0))\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 satellite data\")\n",
    "    model = None\n",
    "\n",
    "print(\"\\n\" + \"=\"*70)\n",
    "print(\"📝 NOTE: Model này dự đoán PHÂN LOẠI SỬ DỤNG ĐẤT (8 lớp)\")\n",
    "print(\"    NDVI là một trong các features đầu vào, không phải mục tiêu dự đoán\")\n",
    "print(\"    Sau khi predict → có thể hiển thị NDVI map như chỉ số phụ\")\n",
    "print(\"=\"*70)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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 WITH METADATA\n",
    "print(\"=\"*70)\n",
    "print(\"MODEL SAVING\")\n",
    "print(\"=\"*70)\n",
    "\n",
    "if model is not None:\n",
    "    print(\"\\n🔄 Saving trained LAND USE CLASSIFICATION model with metadata...\")\n",
    "    try:\n",
    "        from datetime import datetime\n",
    "        \n",
    "        # Prepare metadata for ModelManager\n",
    "        metadata = {\n",
    "            \"timestamp\": datetime.now().isoformat(),\n",
    "            \"data_source\": \"Local S3 ODC (Open Data Cube)\",\n",
    "            \"collections\": [\"sentinel-2-l2a\"],\n",
    "            \"features\": features_to_use,  # All features used\n",
    "            \"feature_mode\": \"extended\",  # Using extended aggregate features\n",
    "            \"training_samples\": len(X_train),\n",
    "            \"testing_samples\": len(X_test),\n",
    "            \"test_size\": 0.2,\n",
    "            \"train_accuracy\": float(model.score(X_train, y_train)),\n",
    "            \"test_accuracy\": float(accuracy),\n",
    "            \"model_type\": \"random_forest\",\n",
    "            \"device\": \"cpu\",\n",
    "            \"n_estimators\": 200,\n",
    "            \"max_depth\": 30,\n",
    "            \"learning_rate\": None,\n",
    "            \"cnn_epochs\": None,\n",
    "            \"n_features\": X_train.shape[1],\n",
    "            \"n_classes\": len(np.unique(y)),\n",
    "            \"class_names\": list(label_mapping.keys()),\n",
    "            \"classification_report\": classification_report(y_test, y_pred, \n",
    "                                                          target_names=class_names, \n",
    "                                                          output_dict=True,\n",
    "                                                          zero_division=0),\n",
    "            \"bbox\": None,\n",
    "            \"time_range\": f\"{date_range[0]}/{date_range[1]}\",\n",
    "            \"resolution\": 10,\n",
    "            \"notes\": \"LAND USE CLASSIFICATION model trained from 01.train_ODC.ipynb. Predicts 8 land use classes using multiple spectral indices. NDVI is one of the input features, not the prediction target.\"\n",
    "        }\n",
    "        \n",
    "        # Save model with metadata using updated save_model function\n",
    "        save_model(\"model_land_use_odc.joblib\", model, metadata=metadata, label_encoder=None)\n",
    "        \n",
    "        print(\"✅ Model saved to model_train/model_land_use_odc.joblib\")\n",
    "        print(f\"   - Purpose: Land Use Classification (8 classes)\")\n",
    "        print(f\"   - Features: {len(features_to_use)} ({', '.join(features_to_use[:3])}...)\")\n",
    "        print(f\"   - Train Accuracy: {metadata['train_accuracy']*100:.2f}%\")\n",
    "        print(f\"   - Test Accuracy: {metadata['test_accuracy']*100:.2f}%\")\n",
    "        print(f\"   - Classes: {metadata['n_classes']}\")\n",
    "        print(f\"\\n📝 NDVI là một trong các features, không phải prediction target\")\n",
    "        print(f\"   Sau khi predict → có thể tính NDVI map riêng để hiển thị\")\n",
    "    except Exception as e:\n",
    "        print(f\"❌ Error saving model: {e}\")\n",
    "        import traceback\n",
    "        traceback.print_exc()\n",
    "else:\n",
    "    print(\"❌ No model to save\")\n",
    "\n",
    "print(\"=\"*70)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a8579f4",
   "metadata": {},
   "source": [
    "# 📖 Hướng dẫn sử dụng Model\n",
    "\n",
    "## Mục đích của Model\n",
    "\n",
    "Model này được train để **DỰ ĐOÁN PHÂN LOẠI SỬ DỤNG ĐẤT** với 8 lớp:\n",
    "\n",
    "1. **Lua tom** (0) - Lúa tôm\n",
    "2. **Lua** (1) - Lúa\n",
    "3. **CHN** (2) - Cây hàng năm\n",
    "4. **CLN** (3) - Cây lâu năm  \n",
    "5. **TS** (4) - Thủy sản\n",
    "6. **Song** (5) - Sông\n",
    "7. **Dat xay dung** (6) - Đất xây dựng\n",
    "8. **Rung** (7) - Rừng\n",
    "\n",
    "## Features đầu vào\n",
    "\n",
    "Model sử dụng **nhiều spectral indices** làm features:\n",
    "- NDVI (mean, min, max, std, range)\n",
    "- NDWI (mean)\n",
    "- NDBI (mean)\n",
    "- EVI (mean)\n",
    "\n",
    "## NDVI là gì trong hệ thống này?\n",
    "\n",
    "⚠️ **QUAN TRỌNG**: NDVI **KHÔNG PHẢI** là mục tiêu dự đoán!\n",
    "\n",
    "- **NDVI là INPUT FEATURE**: Một trong các chỉ số dùng để train model\n",
    "- **Mục tiêu dự đoán**: Phân loại sử dụng đất (8 lớp)\n",
    "- **NDVI map**: Có thể hiển thị NDVI map như chỉ số phụ sau khi predict xong\n",
    "\n",
    "## Workflow Prediction\n",
    "\n",
    "```python\n",
    "# 1. Load model\n",
    "model, label_encoder, metadata = model_manager.load_model(\"model_land_use_odc.joblib\")\n",
    "\n",
    "# 2. Extract features từ satellite data\n",
    "features = extract_features(satellite_data)  # NDVI, NDWI, NDBI, EVI\n",
    "\n",
    "# 3. Predict land use classification\n",
    "land_use_prediction = model.predict(features)\n",
    "# → Kết quả: Mảng với giá trị 0-7 (8 lớp sử dụng đất)\n",
    "\n",
    "# 4. (Optional) Tính NDVI map riêng để hiển thị\n",
    "ndvi_map = (NIR - Red) / (NIR + Red)\n",
    "# → NDVI map chỉ để visualize, không phải prediction target\n",
    "```\n",
    "\n",
    "## So sánh với approach cũ\n",
    "\n",
    "| Approach | Features | Target | NDVI Role |\n",
    "|----------|----------|--------|-----------|\n",
    "| ❌ Cũ (sai) | Chỉ NDVI | 8 lớp đất | Input duy nhất |\n",
    "| ✅ Mới (đúng) | NDVI + NDWI + NDBI + EVI | 8 lớp đất | Một trong nhiều features |\n",
    "\n",
    "## Test Model\n",
    "\n",
    "```python\n",
    "# Test với website\n",
    "# 1. Upload model_land_use_odc.joblib lên server\n",
    "# 2. Chọn model trong prediction interface\n",
    "# 3. Chọn vùng và thời gian\n",
    "# 4. System sẽ tự động:\n",
    "#    - Extract features (NDVI, NDWI, NDBI, EVI)\n",
    "#    - Predict land use classification\n",
    "#    - (Optional) Generate NDVI visualization map\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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",
      "✅ Cleanup complete\n",
      "\n",
      "======================================================================\n",
      "✅ PIPELINE COMPLETE\n",
      "======================================================================\n"
     ]
    }
   ],
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef65aa25-9032-4e1c-8808-64747e8005c6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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