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{
  "type": "Feature",
  "stac_version": "1.1.0",
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  "id": "ENSEMBLE_2MODELS_FLEXIBLE_20251202",
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    "datetime": "2025-12-02T12:54:24Z",
    "created": "2025-12-02T12:54:24Z",
    "updated": "2025-12-02T12:54:24Z",
    "description": "Flexible ensemble of 2 models (1dpwunetpp, unetpp) with runtime aggregation (mean/max/min) and uncertainty quantification for cloud detection in VGT-1, VGT-2, and PROBA-V satellite imagery. Models are loaded separately and combined at inference time.",
    "title": "Ensemble Cloud Detection Model (2 Models + Uncertainty) - VGT1/VGT2/Proba-V",
    "mlm:name": "ensemble_2models_flexible_fdr4vgt_cloudmask",
    "mlm:architecture": "Flexible Ensemble (runtime Mean/Max/Min + Uncertainty): 1dpwunetpp, unetpp",
    "mlm:tasks": [
      "semantic-segmentation",
      "uncertainty-quantification"
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    "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": 4,
    "mlm:pretrained": true,
    "mlm:input": [
      {
        "name": "VGT_PROBA_TOC_reflectance",
        "bands": [
          "Blue (B0, ~450nm)",
          "Red (B2, ~645nm)",
          "Near-Infrared (B3, ~835nm)",
          "SWIR (MIR, ~1665nm)"
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        "input": {
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          "dim_order": [
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        "norm": {
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          "range": [
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    "mlm:output": [
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        "tasks": [
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            "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. Recommended threshold: 0.5. Returns tuple: (probabilities, uncertainty)",
        "standard_threshold": 0.5,
        "recommended_threshold": 0.5,
        "value_range": [
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          1.0
        ],
        "description": "Per-pixel mean probability across 2 ensemble models. Built-in sigmoid activation. Values close to 1.0 indicate high confidence of cloud."
      },
      {
        "name": "prediction_uncertainty",
        "tasks": [
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        },
        "value_range": [
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        ],
        "description": "Normalized standard deviation across 2 ensemble members. Values close to 1.0 indicate high disagreement between models (high uncertainty). Automatically returned as second element of output tuple."
      }
    ],
    "custom:export_format": "torch.export.pt2",
    "custom:has_sigmoid": true,
    "custom:sigmoid_location": "built-in wrapper",
    "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:export_datetime": "2025-12-02T12:54:24Z",
    "custom:ensemble_size": 2,
    "custom:ensemble_members": [
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    ],
    "custom:ensemble_strategy": "Flexible runtime aggregation - supports mean/max/min modes with uncertainty quantification",
    "custom:ensemble_fused": false,
    "custom:returns_tuple": true,
    "custom:tuple_format": "(probabilities, uncertainty)"
  },
  "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 with mean/max/min aggregation.",
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    "example_data": {
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      "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": [
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      "title": "Model 1: 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": [
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    "model_02_unetpp": {
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      "title": "Model 2: unetpp_fdr4vgt_cloudmask_ft",
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