framework: tensorflow
framework_detail: tensorflow_keras
weights_filename: model.weights.h5
weights_format: legacy_keras_h5
architecture:
  name: resunet
  implementation: src/colab_grd_gee/tf_model.py
  input_shape: [128, 128, 2]
  input_layout: NHWC
  input_channels: 2
  input_order: [t1, t2]
  output_channels: 2
  output_layout: NHWC
preprocessing:
  gee_collection: COPERNICUS/S1_GRD
  gee_instrument_mode: IW
  gee_default_orbit_pass: ASCENDING
  gee_default_polarization: VV
  gee_composite: median
  gee_download_scale_meters: 10
  gee_download_crs: EPSG:4326
  gee_download_scale: linear_sigma0
  gee_db_to_linear: "10 ** (db / 10)"
  model_input_scale: linear_sigma0_from_gee
  model_preprocessing:
    - "convert linear sigma0 to dB with 10 * log10(linear + eps)"
    - "clip dB values to [-20, 0]"
    - "normalize clipped dB to [0, 1]"
  db_min: -20.0
  db_max: 0.0
  channel_order: [t1, t2]
tiling:
  patch_size: 128
  stride: 32
  batch_size: 8
  aggregation: kaiser
output:
  name: predicted_coherence
  selected_channel: 0
  range: [0.0, 1.0]
  postprocessing:
    - "select first model output channel"
    - "aggregate overlapping patches"
    - "clip predicted coherence to [0, 1]"
training_metadata:
  source_weight_set: GEE
  original_architecture_name: resunet
  original_input_features:
    - SigmaNoughtPrimary_VV
    - SigmaNoughtSecondary_VV
  original_output_features:
    - Coherence_VV
    - Uncertainty