| { | |
| "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": "1DPWDEEPLABV3_CLOUDMASK_FT_2025-10-27", | |
| "properties": { | |
| "datetime": "2025-10-27T09:09:20Z", | |
| "created": "2025-10-27T09:09:20Z", | |
| "updated": "2025-12-11T17:27:11.790336Z", | |
| "title": "1DPWDEEPLABV3 Cloud Detection Model", | |
| "description": "DeepLabV3 + PixelWise MLP architecture fine-tuned for cloud detection in VGT-1, VGT-2, and PROBA-V satellite imagery from the FDR4VGT harmonized dataset.", | |
| "mlm:name": "1dpwdeeplabv3_fdr4vgt_cloudmask_ft", | |
| "mlm:architecture": "DeepLabV3 + PixelWise MLP", | |
| "mlm:tasks": [ | |
| "semantic-segmentation" | |
| ], | |
| "mlm:framework": "pytorch", | |
| "mlm:framework_version": "2.5.1+cu121", | |
| "mlm:accelerator": "cuda", | |
| "mlm:memory_size": 90102208, | |
| "mlm:batch_size_suggestion": 32, | |
| "mlm:total_parameters": 12649538, | |
| "mlm:input": [ | |
| { | |
| "name": "reflectance", | |
| "bands": [ | |
| "Blue", | |
| "Red", | |
| "NIR", | |
| "SWIR" | |
| ], | |
| "input": { | |
| "shape": [ | |
| -1, | |
| 4, | |
| 512, | |
| 512 | |
| ], | |
| "dim_order": [ | |
| "batch", | |
| "channel", | |
| "height", | |
| "width" | |
| ], | |
| "data_type": "float32" | |
| }, | |
| "norm": { | |
| "type": "raw_toc_reflectance", | |
| "range": [ | |
| 0, | |
| 10000 | |
| ] | |
| } | |
| } | |
| ], | |
| "mlm:output": [ | |
| { | |
| "name": "cloud_probability", | |
| "result": { | |
| "shape": [ | |
| -1, | |
| 1, | |
| 512, | |
| 512 | |
| ], | |
| "dim_order": [ | |
| "batch", | |
| "channel", | |
| "height", | |
| "width" | |
| ], | |
| "data_type": "float32" | |
| }, | |
| "recommended_threshold": 0.4848, | |
| "standard_threshold": 0.5, | |
| "value_range": [ | |
| 0.0, | |
| 1.0 | |
| ], | |
| "description": "Per-pixel cloud probability (Sigmoid activated)." | |
| } | |
| ], | |
| "mlm:hyperparameters": { | |
| "batch_size": "Dynamic (1-256)", | |
| "learning_rate": 0.0001, | |
| "training_epochs": 25, | |
| "final_val_loss": 0.0611 | |
| }, | |
| "custom:dynamic_batch": true, | |
| "custom:batch_range": [ | |
| 1, | |
| 256 | |
| ], | |
| "custom:sensors": [ | |
| "VGT-1", | |
| "VGT-2", | |
| "PROBA-V" | |
| ], | |
| "custom:spatial_resolution": "1km (VGT) / 300m (PROBA-V)", | |
| "file:size": 60068139, | |
| "dependencies": [ | |
| "torch>=2.0.0" | |
| ] | |
| }, | |
| "assets": { | |
| "model": { | |
| "href": "https://huggingface.co/isp-uv-es/FDR4VGT-CLOUD/resolve/main/single/multisensor_single_1dpwdeeplabv3.pt2", | |
| "type": "application/octet-stream; application=pytorch", | |
| "title": "PyTorch model weights (Dynamic Batch)", | |
| "roles": [ | |
| "mlm:model", | |
| "data" | |
| ] | |
| }, | |
| "load": { | |
| "href": "https://huggingface.co/isp-uv-es/FDR4VGT-CLOUD/resolve/main/single/load.py", | |
| "type": "application/x-python-code", | |
| "roles": [ | |
| "code" | |
| ] | |
| } | |
| }, | |
| "links": [ | |
| { | |
| "rel": "license", | |
| "href": "https://mit-license.org/", | |
| "type": "text/html" | |
| } | |
| ] | |
| } |