Upload: ensemble_spot.json

ensemble/ensemble_spot.json

@@ -0,0 +1,274 @@
+{
+  "type": "Feature",
+  "stac_version": "1.1.0",
+  "stac_extensions": [
+    "https://stac-extensions.github.io/mlm/v1.5.0/schema.json",
+    "https://stac-extensions.github.io/file/v2.1.0/schema.json"
+  ],
+  "id": "ENSEMBLE_6MODELS_CLOUDMASK_FT_20251118",
+  "geometry": {
+    "type": "Polygon",
+    "coordinates": [
+      [
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+          -90.0
+        ],
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+          90.0
+        ],
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+          90.0
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+          -90.0
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+          -180.0,
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+      ]
+    ]
+  },
+  "bbox": [
+    -180,
+    -90,
+    180,
+    90
+  ],
+  "properties": {
+    "datetime": "2025-11-18T12:32:38Z",
+    "created": "2025-11-18T12:32:38Z",
+    "updated": "2025-11-18T12:32:38Z",
+    "description": "Ensemble of 6 fine-tuned DeepLabV3/UNet++/LinkNet models for cloud detection in VGT-1, VGT-2, and PROBA-V imagery. Use load.py for inference.",
+    "title": "Ensemble Cloud Detection Model (6 Models) - VGT1/VGT2/Proba-V",
+    "mlm:name": "ensemble_fdr4vgt_cloudmask_ft",
+    "mlm:architecture": "Ensemble (Mean/Max/Min aggregation) of 6 segmentation models",
+    "mlm:tasks": [
+      "semantic-segmentation"
+    ],
+    "mlm:framework": "pytorch",
+    "mlm:framework_version": "2.5.1+cu121",
+    "mlm:accelerator": "cuda",
+    "mlm:accelerator_constrained": false,
+    "mlm:accelerator_summary": "NVIDIA GPU with CUDA support (compute capability >= 7.0)",
+    "mlm:accelerator_count": 1,
+    "mlm:batch_size_suggestion": 8,
+    "mlm:pretrained": true,
+    "mlm:input": [
+      {
+        "name": "VGT_PROBA_TOC_reflectance",
+        "bands": [
+          "Blue (B0, ~450nm)",
+          "Red (B2, ~645nm)",
+          "Near-Infrared (B3, ~835nm)",
+          "SWIR (MIR, ~1665nm)"
+        ],
+        "input": {
+          "shape": [
+            -1,
+            4,
+            512,
+            512
+          ],
+          "dim_order": [
+            "batch",
+            "channel",
+            "height",
+            "width"
+          ],
+          "data_type": "float32"
+        },
+        "norm": {
+          "type": "raw_toc_reflectance",
+          "range": [
+            0,
+            10000
+          ],
+          "description": "Raw Top-of-Canopy reflectance values scaled by 10000"
+        },
+        "pre_processing_function": null
+      }
+    ],
+    "mlm:output": [
+      {
+        "name": "cloud_probability",
+        "tasks": [
+          "semantic-segmentation"
+        ],
+        "result": {
+          "shape": [
+            -1,
+            1,
+            512,
+            512
+          ],
+          "dim_order": [
+            "batch",
+            "channel",
+            "height",
+            "width"
+          ],
+          "data_type": "float32"
+        },
+        "classification:classes": [
+          {
+            "value": 0.0,
+            "name": "clear",
+            "description": "Clear sky (may contain cloud shadows)",
+            "color_hint": "00000000"
+          },
+          {
+            "value": 1.0,
+            "name": "cloud",
+            "description": "Cloud present",
+            "color_hint": "FFFF00"
+          }
+        ],
+        "post_processing_function": "Apply threshold to get binary mask. Standard threshold: 0.5. Recommended (balanced) threshold: 0.4.",
+        "standard_threshold": 0.5,
+        "recommended_threshold": 0.596,
+        "value_range": [
+          0.0,
+          1.0
+        ],
+        "description": "Per-pixel probability of cloud presence. Built-in sigmoid activation. Values close to 1.0 indicate high confidence of cloud."
+      }
+    ],
+    "custom:export_format": "torch.export.pt2",
+    "custom:has_sigmoid": true,
+    "custom:sigmoid_location": "built-in wrapper",
+    "custom:export_datetime": "2025-11-18T12:32:38Z",
+    "custom:project": "FDR4VGT",
+    "custom:project_url": "https://fdr4vgt.eu/",
+    "custom:sensors": [
+      "VGT-1",
+      "VGT-2",
+      "PROBA-V"
+    ],
+    "custom:sensor_notes": "Model applicable to SPOT-VGT1, SPOT-VGT2, and PROBA-V imagery",
+    "custom:spatial_resolution": "1km",
+    "custom:tile_size": 512,
+    "custom:recommended_overlap": 64,
+    "custom:applicable_start": "1998-03-01T00:00:00Z",
+    "custom:applicable_end": null,
+    "dependencies": [
+      "torch>=2.0.0",
+      "segmentation-models-pytorch>=0.3.0",
+      "pytorch-lightning>=2.0.0"
+    ],
+    "custom:ensemble_size": 6,
+    "custom:ensemble_strategy": "Mean probability aggregation (default), supports Max/Min modes"
+  },
+  "links": [
+    {
+      "rel": "about",
+      "href": "https://fdr4vgt.eu/",
+      "type": "text/html",
+      "title": "FDR4VGT Project - Harmonized VGT Data Record"
+    },
+    {
+      "rel": "license",
+      "href": "https://creativecommons.org/licenses/by/4.0/",
+      "type": "text/html",
+      "title": "CC-BY-4.0 License"
+    }
+  ],
+  "assets": {
+    "load": {
+      "href": "https://huggingface.co/isp-uv-es/FDR4VGT-CLOUD/resolve/main/single/load.py",
+      "type": "application/x-python-code",
+      "title": "Ensemble model loader",
+      "description": "Python code to load all models and combine them into an EnsembleModel class.",
+      "roles": [
+        "code"
+      ]
+    },
+    "example_data": {
+      "href": "https://huggingface.co/isp-uv-es/FDR4VGT-CLOUD/resolve/main/single/example_data.safetensor",
+      "type": "application/octet-stream; application=safetensors",
+      "title": "Example VGT/PROBA-V image",
+      "description": "Example VGT/PROBA-V Top-of-Canopy reflectance image for model inference.",
+      "roles": [
+        "mlm:example_data",
+        "data"
+      ]
+    },
+    "model_01_spot_1dpwseg": {
+      "href": "https://huggingface.co/isp-uv-es/FDR4VGT-CLOUD/resolve/main/single/spot_1dpwseg.pt2",
+      "type": "application/octet-stream; application=pytorch",
+      "title": "Model 1: 1dpwseg_fdr4vgt_cloudmask_ft",
+      "description": "The weights of the 1DPWSEG model in torch.export .pt2 format with built-in sigmoid activation.",
+      "mlm:artifact_type": "torch.export.pt2",
+      "roles": [
+        "mlm:model",
+        "mlm:weights",
+        "data"
+      ]
+    },
+    "model_02_spot_segformer": {
+      "href": "https://huggingface.co/isp-uv-es/FDR4VGT-CLOUD/resolve/main/single/spot_segformer.pt2",
+      "type": "application/octet-stream; application=pytorch",
+      "title": "Model 2: segformer_fdr4vgt_cloudmask_ft",
+      "description": "The weights of the SEGFORMER model in torch.export .pt2 format with built-in sigmoid activation.",
+      "mlm:artifact_type": "torch.export.pt2",
+      "roles": [
+        "mlm:model",
+        "mlm:weights",
+        "data"
+      ]
+    },
+    "model_03_spot_unetpp": {
+      "href": "https://huggingface.co/isp-uv-es/FDR4VGT-CLOUD/resolve/main/single/spot_unetpp.pt2",
+      "type": "application/octet-stream; application=pytorch",
+      "title": "Model 3: unetpp_fdr4vgt_cloudmask_ft",
+      "description": "The weights of the UNETPP model in torch.export .pt2 format with built-in sigmoid activation.",
+      "mlm:artifact_type": "torch.export.pt2",
+      "roles": [
+        "mlm:model",
+        "mlm:weights",
+        "data"
+      ]
+    },
+    "model_04_spot_1dpwunet": {
+      "href": "https://huggingface.co/isp-uv-es/FDR4VGT-CLOUD/resolve/main/single/spot_1dpwunet.pt2",
+      "type": "application/octet-stream; application=pytorch",
+      "title": "Model 4: 1dpwunet_fdr4vgt_cloudmask_ft",
+      "description": "The weights of the 1DPWUNET model in torch.export .pt2 format with built-in sigmoid activation.",
+      "mlm:artifact_type": "torch.export.pt2",
+      "roles": [
+        "mlm:model",
+        "mlm:weights",
+        "data"
+      ]
+    },
+    "model_05_spot_1dpwunetpp": {
+      "href": "https://huggingface.co/isp-uv-es/FDR4VGT-CLOUD/resolve/main/single/spot_1dpwunetpp.pt2",
+      "type": "application/octet-stream; application=pytorch",
+      "title": "Model 5: 1dpwunetpp_fdr4vgt_cloudmask_ft",
+      "description": "The weights of the 1DPWUNETPP model in torch.export .pt2 format with built-in sigmoid activation.",
+      "mlm:artifact_type": "torch.export.pt2",
+      "roles": [
+        "mlm:model",
+        "mlm:weights",
+        "data"
+      ]
+    },
+    "model_06_spot_1dpwdeeplabv3": {
+      "href": "https://huggingface.co/isp-uv-es/FDR4VGT-CLOUD/resolve/main/single/spot_1dpwdeeplabv3.pt2",
+      "type": "application/octet-stream; application=pytorch",
+      "title": "Model 6: 1dpwdeeplabv3_fdr4vgt_cloudmask_ft",
+      "description": "The weights of the 1DPWDEEPLABV3 model in torch.export .pt2 format with built-in sigmoid activation.",
+      "mlm:artifact_type": "torch.export.pt2",
+      "roles": [
+        "mlm:model",
+        "mlm:weights",
+        "data"
+      ]
+    }
+  },
+  "collection": "FDR4VGT_CloudMask_Ensemble"
+}