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README.md

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+---
+library_name: keras
+tags:
+  - remote-sensing
+  - sar
+  - sentinel-1
+  - coherence
+  - earth-engine
+---
+
+# Beyond Backscatter GRD/GEE Coherence Estimator
+
+This model package contains the TensorFlow/Keras GRD/GEE model weights for **Beyond Backscatter: InSAR Coherence from Detected SAR Images**.
+
+GitHub repository:
+https://github.com/FPSica/BeyondBackscatter
+
+Public Colab notebook:
+https://colab.research.google.com/github/FPSica/BeyondBackscatter/blob/main/notebooks/back2coh_grd_gee_colab.ipynb
+
+## Task
+
+Predict an InSAR-like coherence map from two detected Sentinel-1 GRD/GEE SAR backscatter images.
+
+## Inputs
+
+The public notebook downloads two Sentinel-1 GRD sigma0 backscatter images from Google Earth Engine in linear scale. The default polarization is `VV`.
+
+Earth Engine preprocessing:
+
+- collection: `COPERNICUS/S1_GRD`;
+- acquisition mode: `IW`;
+- orbit pass filtering, default `ASCENDING`;
+- optional relative orbit filtering;
+- two user-selected date windows;
+- median composite for each date window;
+- dB-to-linear conversion using `10 ** (db / 10)`;
+- selected polarization;
+- clipped region of interest;
+- 10 m output scale by default.
+
+Model preprocessing:
+
+- convert downloaded linear sigma0 back to dB with `10 * log10(linear + eps)`;
+- clip to `[-20, 0]` dB;
+- normalize to `[0, 1]`;
+- channel order: `[t1, t2]`;
+- tiled inference with 128 x 128 patches and Kaiser-window aggregation.
+
+## Outputs
+
+The model outputs a predicted coherence map in `[0, 1]`. The public notebook saves the map as GeoTIFF, PNG, and NumPy products, preserving georeferencing from the downloaded Sentinel-1 inputs.
+
+## Files
+
+- `model.weights.h5`: real GRD/GEE TensorFlow/Keras weights.
+- `config.yaml`: model, preprocessing, tiling, and output conventions.
+- `model_metadata.json`: lightweight public packaging metadata.
+
+The TensorFlow/Keras architecture implementation is provided by the GitHub repository in `src/colab_grd_gee/tf_model.py`.
+
+## Limitations
+
+- This is not the SLC-based workflow.
+- This is not true interferometric processing from complex SLC data.
+- The pseudo-RGB products produced by the notebook are SAR/coherence visualizations, not optical imagery.
+- Earth Engine authentication and a valid Earth Engine-enabled Google Cloud project are required to run the full notebook.
+- Start with a small ROI before processing larger areas.
+
+## Citation
+
+Beyond Backscatter: InSAR Coherence from Detected SAR Images  
+Francescopaolo Sica, Andrea Pulella, Michael Schmitt
+
+## License
+
+The GitHub code repository is MIT licensed. The model-weight license should be confirmed by the authors before redistribution or downstream release.