unet.json

{
  "type": "Feature",
  "stac_version": "1.1.0",
  "stac_extensions": [
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    "https://stac-extensions.github.io/file/v2.1.0/schema.json"
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  "id": "MSS_CLOUDMASK_UNET_EFFB3",
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  "properties": {
    "datetime": "2026-01-18T22:42:31.441233Z",
    "created": "2026-01-18T22:42:31.441233Z",
    "updated": "2026-01-19T01:01:38.488397Z",
    "title": "MSS Cloud Detection Model (UNet-EfficientNetB3)",
    "description": "UNet architecture with EfficientNet-B3 encoder for cloud detection in Landsat MSS (Multispectral Scanner) imagery. Trained on CloudSEN12 data emulated to MSS spectral bands using satharmony package. Detects 4 classes: clear, thin cloud, thick cloud, and shadow.",
    "mlm:name": "mss_cloudmask_unet_effb3",
    "mlm:architecture": "UNet with EfficientNet-B3 encoder + SCSE attention",
    "mlm:tasks": [
      "semantic-segmentation",
      "cloud-detection"
    ],
    "mlm:framework": "pytorch",
    "mlm:framework_version": "2.5.1+cu121",
    "mlm:accelerator": "cuda",
    "mlm:memory_size": 309827650,
    "mlm:batch_size_suggestion": 8,
    "mlm:total_parameters": 13223490,
    "mlm:input": [
      {
        "name": "mss_reflectance",
        "bands": [
          "Green (500-600nm)",
          "Red (600-700nm)",
          "NIR1 (700-800nm)",
          "NIR2 (800-1100nm)"
        ],
        "input": {
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            "W"
          ],
          "dim_order": [
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            "channel",
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          "data_type": "float32"
        },
        "norm": {
          "type": "reflectance",
          "range": [
            0.0,
            1.0
          ],
          "description": "TOA reflectance normalized to [0, 1]. DN values should be divided by 10000."
        },
        "preprocessing": "Divide DN by 10000 to get reflectance in [0, 1]"
      }
    ],
    "mlm:output": [
      {
        "name": "cloud_mask",
        "classes": [
          {
            "id": 0,
            "name": "clear",
            "description": "Clear sky"
          },
          {
            "id": 1,
            "name": "thin_cloud",
            "description": "Thin/cirrus clouds"
          },
          {
            "id": 2,
            "name": "thick_cloud",
            "description": "Thick/opaque clouds"
          },
          {
            "id": 3,
            "name": "shadow",
            "description": "Cloud shadow"
          }
        ],
        "result": {
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          ],
          "dim_order": [
            "batch",
            "class",
            "height",
            "width"
          ],
          "data_type": "float32"
        },
        "description": "Per-pixel logits for 4 classes. Use argmax to get class labels, or softmax for probabilities.",
        "postprocessing": "Apply argmax(dim=1) to get class labels (0-3), or softmax(dim=1) for probabilities"
      }
    ],
    "mlm:hyperparameters": {
      "learning_rate": 0.0003,
      "weight_decay": 0.0001,
      "optimizer": "AdamW",
      "scheduler": "CosineAnnealingWarmRestarts",
      "batch_size": 256,
      "training_epochs": 55,
      "final_val_iou": 0.6164,
      "loss_function": "CrossEntropyLoss",
      "encoder_depth": 5,
      "decoder_attention": "SCSE"
    },
    "custom:sensor": "Landsat MSS",
    "custom:spatial_resolution": "60m",
    "custom:temporal_coverage": "1972-2013",
    "custom:training_data": "CloudSEN12 emulated to MSS bands",
    "custom:emulator": "satharmony",
    "custom:project": "QA4EO-2",
    "custom:project_url": "https://github.com/IPL-UV/qa4eo",
    "file:size": 154913825,
    "dependencies": [
      "torch>=2.0.0",
      "pytorch-lightning>=2.0.0",
      "segmentation-models-pytorch>=0.3.0",
      "rasterio>=1.3.0",
      "numpy>=1.21.0"
    ]
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  "assets": {
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      "href": "https://huggingface.co/isp-uv-es/QA4EO-2/resolve/main/unet.ckpt",
      "type": "application/octet-stream",
      "title": "PyTorch Lightning checkpoint",
      "roles": [
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        "mlm:weights"
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    "load": {
      "href": "https://huggingface.co/isp-uv-es/QA4EO-2/resolve/main/load.py",
      "type": "application/x-python-code",
      "title": "Model loading and inference functions",
      "roles": [
        "mlm:inference-code"
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    }
  },
  "links": [
    {
      "rel": "about",
      "href": "https://github.com/IPL-UV/qa4eo",
      "type": "text/html",
      "title": "Project repository"
    },
    {
      "rel": "license",
      "href": "https://creativecommons.org/licenses/by/4.0/",
      "type": "text/html",
      "title": "CC-BY-4.0"
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}