{
  "model_name": "PrithviEO",
  "model_type": "prithvi_eo",
  "architectures": ["PrithviEO2", "CoordinateEncoder", "Transformer"],
  "framework": "PyTorch",
  "domain": "earth-observation",
  "task": "multi-temporal-remote-sensing-representation-learning",
  "implementation": {
    "entry_point": "model/prithvi_eo.py",
    "scope": "engineering reproduction of the Prithvi-EO-2.0 temporal-location masked autoencoder",
    "train_script": "scripts/train.py",
    "inference_script": "scripts/inference.py",
    "evaluation_script": "scripts/result.py",
    "synthetic_data_script": "scripts/fake_data.py"
  },
  "architecture": {
    "family": "three-dimensional vision-transformer masked autoencoder",
    "input_size": [4, 224, 224],
    "patch_size": [1, 16, 16],
    "in_channels": 6,
    "mask_ratio": 0.75,
    "encoder_dim": 96,
    "encoder_depth": 2,
    "encoder_heads": 4,
    "decoder_dim": 64,
    "decoder_depth": 1,
    "decoder_heads": 4,
    "metadata": ["year", "day_of_year", "latitude", "longitude"],
    "outputs": ["loss", "embedding", "patch_embeddings", "reconstruction", "mask"]
  },
  "data": {
    "datasets": ["Harmonized Landsat Sentinel-2"],
    "protocol": "prithvi_eo_2_synthetic_engineering_v1",
    "format": "NPZ",
    "train_file": "data/train.npz",
    "test_file": "data/test.npz",
    "input_shape": ["N", 6, 4, 224, 224],
    "temporal_shape": ["N", 4, 2],
    "location_shape": ["N", 2],
    "bands": ["B02", "B03", "B04", "B8A", "B11", "B12"],
    "required_metadata": ["format_version", "data_source", "temporal_coords", "location_coords"]
  },
  "configuration_sources": ["conf/config.yaml", "model/prithvi_eo.py", "scripts/fake_data.py", "scripts/train.py", "scripts/inference.py", "scripts/result.py"]
}