Forestfire scenario failure

algorithm_catalog/vito/random_forest_firemapping/benchmark_scenarios/random_forest_firemapping.json

@@ -84,7 +84,7 @@
                         "2023-09-15"
                     ]
                 },
-                "namespace": "https://raw.githubusercontent.com/ESA-APEx/apex_algorithms/0962bf79f836859e701fa7437307240ef689ff2e/algorithm_catalog/vito/random_forest_firemapping/openeo_udp/random_forest_firemapping.json",
+                "namespace": "https://raw.githubusercontent.com/ESA-APEx/apex_algorithms/refs/heads/main/algorithm_catalog/vito/random_forest_firemapping/openeo_udp/random_forest_firemapping.json",
                 "process_id": "random_forest_firemapping",
                 "result": true
             }

algorithm_catalog/vito/random_forest_firemapping/openeo_udp/random_forest_firemapping.json

@@ -329,7 +329,7 @@
                                     "from_parameter": "data"
                                 },
                                 "runtime": "Python",
-                                "udf": "import xarray\nimport numpy as np\nfrom skimage.feature import graycomatrix, graycoprops\nfrom openeo.metadata import CollectionMetadata\n\n\ndef apply_metadata(metadata: CollectionMetadata, context: dict) -> CollectionMetadata:\n    return metadata.rename_labels(\n        dimension = \"bands\",\n        target = [\"contrast\",\"variance\",\"NDFI\"]\n    )\n\n\ndef apply_datacube(cube: xarray.DataArray, context: dict) -> xarray.DataArray:\n    \"\"\"\n    Applies spatial texture analysis and spectral index computation to a Sentinel-2 data cube.\n\n    Computes:\n    - NDFI (Normalized Difference Fraction Index) from bands B08 and B12\n    - Texture features (contrast and variance) using Gray-Level Co-occurrence Matrix (GLCM)\n\n    Args:\n        cube (xarray.DataArray): A 3D data cube with dimensions (bands, y, x) containing at least bands B08 and B12.\n        context (dict): A context dictionary (currently unused, included for API compatibility).\n\n    Returns:\n        xarray.DataArray: A new data cube with dimensions (bands, y, x) containing:\n                          - 'contrast': GLCM contrast\n                          - 'variance': GLCM variance\n                          - 'NDFI': Normalised Difference Fire Index\n    \"\"\"\n    \n    # Parameters\n    window_size = context.get(\"padding_window_size\", 33)  # Default to 33 if not provided\n    pad = window_size // 2\n    levels = 256  # For 8-bit images\n    \n    # Load Data\n    # data = cube.values # shape: (t, bands, y, x)\n    \n    #first get NDFI\n    b08 = cube.sel(bands=\"B08\")\n    b12 = cube.sel(bands=\"B12\")\n\n    # Compute mean values\n    avg_b08 = b08.mean()\n    avg_b12 = b12.mean()\n\n    # Calculate NDFI\n    ndfi = ((b12 / avg_b12) - (b08 / avg_b08)) / (b08 / avg_b08)\n    \n    # Padding the image to handle border pixels for GLCM\n    padded = np.pad(b12, pad_width=pad, mode='reflect')\n\n    # Normalize to 0–255 range\n    img_norm = (padded - padded.min()) / (padded.max() - padded.min())\n    padded = (img_norm * 255).astype(np.uint8)\n    \n    # Initialize feature maps\n    shape = b12.shape\n    contrast = np.zeros(shape)\n    variance = np.zeros(shape)\n    \n    for i in range(pad, pad + shape[0]):\n        for j in range(pad, pad + shape[1]):\n            window = padded[i - pad:i + pad + 1, j - pad:j + pad + 1]\n            \n            # Compute GLCM\n            glcm = graycomatrix(window, distances=[5], angles=[0], levels=levels, symmetric=True, normed=True)\n            \n            # Texture features\n            contrast[i - pad, j - pad] = graycoprops(glcm, 'contrast')[0, 0]\n            variance[i - pad, j - pad] = np.var(window)\n\n    all_texture = np.stack([contrast,variance,ndfi])\n    # create a data cube with all the calculated properties\n    textures = xarray.DataArray(\n        data=all_texture,\n        dims=[\"bands\", \"y\", \"x\"],\n        coords={\"bands\": [\"contrast\",\"variance\",\"NDFI\"], \"y\": cube.coords[\"y\"], \"x\": cube.coords[\"x\"]},\n    )\n\n    return textures"
+                                "udf": "# /// script\n# dependencies = [\n#   \"scikit-image\",\n# ]\n# ///\n\nimport xarray\nimport numpy as np\nfrom skimage.feature import graycomatrix, graycoprops\nfrom openeo.metadata import CollectionMetadata\n\n\ndef apply_metadata(metadata: CollectionMetadata, context: dict) -> CollectionMetadata:\n    return metadata.rename_labels(\n        dimension = \"bands\",\n        target = [\"contrast\",\"variance\",\"NDFI\"]\n    )\n\n\ndef apply_datacube(cube: xarray.DataArray, context: dict) -> xarray.DataArray:\n    \"\"\"\n    Applies spatial texture analysis and spectral index computation to a Sentinel-2 data cube.\n\n    Computes:\n    - NDFI (Normalized Difference Fraction Index) from bands B08 and B12\n    - Texture features (contrast and variance) using Gray-Level Co-occurrence Matrix (GLCM)\n\n    Args:\n        cube (xarray.DataArray): A 3D data cube with dimensions (bands, y, x) containing at least bands B08 and B12.\n        context (dict): A context dictionary (currently unused, included for API compatibility).\n\n    Returns:\n        xarray.DataArray: A new data cube with dimensions (bands, y, x) containing:\n                          - 'contrast': GLCM contrast\n                          - 'variance': GLCM variance\n                          - 'NDFI': Normalised Difference Fire Index\n    \"\"\"\n    \n    # Parameters\n    window_size = context.get(\"padding_window_size\", 33)  # Default to 33 if not provided\n    pad = window_size // 2\n    levels = 256  # For 8-bit images\n    \n    # Load Data\n    # data = cube.values # shape: (t, bands, y, x)\n    \n    #first get NDFI\n    b08 = cube.sel(bands=\"B08\")\n    b12 = cube.sel(bands=\"B12\")\n\n    # Compute mean values\n    avg_b08 = b08.mean()\n    avg_b12 = b12.mean()\n\n    # Calculate NDFI\n    ndfi = ((b12 / avg_b12) - (b08 / avg_b08)) / (b08 / avg_b08)\n    \n    # Padding the image to handle border pixels for GLCM\n    padded = np.pad(b12, pad_width=pad, mode='reflect')\n\n    # Normalize to 0–255 range\n    img_norm = (padded - padded.min()) / (padded.max() - padded.min())\n    padded = (img_norm * 255).astype(np.uint8)\n    \n    # Initialize feature maps\n    shape = b12.shape\n    contrast = np.zeros(shape)\n    variance = np.zeros(shape)\n    \n    for i in range(pad, pad + shape[0]):\n        for j in range(pad, pad + shape[1]):\n            window = padded[i - pad:i + pad + 1, j - pad:j + pad + 1]\n            \n            # Compute GLCM\n            glcm = graycomatrix(window, distances=[5], angles=[0], levels=levels, symmetric=True, normed=True)\n            \n            # Texture features\n            contrast[i - pad, j - pad] = graycoprops(glcm, 'contrast')[0, 0]\n            variance[i - pad, j - pad] = np.var(window)\n\n    all_texture = np.stack([contrast,variance,ndfi])\n    # create a data cube with all the calculated properties\n    textures = xarray.DataArray(\n        data=all_texture,\n        dims=[\"bands\", \"y\", \"x\"],\n        coords={\"bands\": [\"contrast\",\"variance\",\"NDFI\"], \"y\": cube.coords[\"y\"], \"x\": cube.coords[\"x\"]},\n    )\n\n    return textures"
                             },
                             "result": true
                         }

algorithm_catalog/vito/random_forest_firemapping/openeo_udp/rf_feature_extraction_ndfi_glcm.py

@@ -1,3 +1,9 @@
+# /// script
+# dependencies = [
+#   "scikit-image",
+# ]
+# ///
+
 import xarray
 import numpy as np
 from skimage.feature import graycomatrix, graycoprops