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README.md

@@ -1,28 +1,93 @@
----
-license: mit
-tags:
-- pytorch
-- pytorch-lightning
-- dem
-- super-resolution
-- remote-sensing
----
-
-# DEM Super-Resolution Model
-
-This is a trained PyTorch Lightning model for super-resolving 30m SRTM DEMs to 10m resolution using Sentinel-2 imagery.
-
-## Model Details
-- **Architecture**: U-Net with ResNet34 encoder
-- **Input Channels**: 7 (SRTM, RGB, NIR, NDVI, mask)
-- **Output**: Residual corrections for DEM super-resolution
-- **Training Data**: McKinley Mine LiDAR DEM
-- **Inference**: Applied to Marrakech, Morocco
+# DEM Super-Resolution
+
+This repository contains a pipeline for generating synthetic high-resolution Digital Elevation Models (DEMs) by super-resolving 30m SRTM data to 10m resolution, fused with Sentinel-2 imagery. The model is trained on high-resolution LiDAR DEM data from McKinley Mine, NM, and applied to generate DEMs for Marrakech, Morocco.
+
+## Overview
+
+The implementation uses an adapted DeepDEM model with a U-Net architecture (ResNet34 encoder) that takes 7 input channels:
+- SRTM DEM (30m)
+- Sentinel-2 RGB bands (10m)
+- Sentinel-2 NIR band (10m)
+- NDVI
+- Nodata mask
+
+The model predicts residual corrections to be added to a smoothed SRTM trend, producing 10m synthetic DEMs.
+
+## Requirements
+
+- Python 3.8+
+- PyTorch 2.0+
+- PyTorch Lightning
+- Segmentation Models PyTorch
+- Rasterio
+- Geopandas
+- Albumentations
+- Earth Engine API (for data acquisition)
+- GDAL
+- Boto3 (for LiDAR data download)
+
+## Installation
+
+1. Clone the repository:
+   ```bash
+   git clone https://github.com/yourusername/DEM_SuperRes.git
+   cd DEM_SuperRes
+   ```
+
+2. Install dependencies:
+   ```bash
+   pip install torch torchvision torchaudio pytorch-lightning segmentation-models-pytorch rasterio geopandas albumentations scipy gdown earthengine-api boto3
+   ```
+
+3. Install system dependencies:
+   ```bash
+   apt-get install libspatialindex-dev libgdal-dev gdal-bin
+   pip install gdal
+   ```
+
+## Data Acquisition
+
+The notebook handles data acquisition from:
+- **SRTM 30m DEM**: CGIAR/SRTM90_V4 via Google Earth Engine
+- **Sentinel-2 10m imagery**: COPERNICUS/S2_SR_HARMONIZED via Google Earth Engine
+- **High-resolution LiDAR DEM**: OpenTopography (McKinley Mine, NM)
+
+Authenticate with Google Earth Engine and ensure access to required datasets.
 
 ## Usage
-```python
-import torch
-from your_model_loading_code import DeepDEMRefinement
 
-model = DeepDEMRefinement.load_from_checkpoint("deepdem_model.ckpt")
-# Use as needed
+1. Open `DEM_SuperRes.ipynb` in Google Colab or Jupyter.
+2. Run cells sequentially to:
+   - Acquire and preprocess training data (McKinley)
+   - Train the model
+   - Acquire and preprocess inference data (Marrakech)
+   - Generate synthetic DEM
+   - Run validation checks
+
+3. Key outputs:
+   - Trained model: `Models/deepdem_model.ckpt`
+   - Synthetic DEM: `synth_dem_marrakech.tif`
+
+## Model Training
+
+- **Architecture**: U-Net with ResNet34 encoder, 7 input channels, 1 output channel (residuals)
+- **Loss**: L1 loss
+- **Optimizer**: Adam (lr=1e-4)
+- **Training**: 5 epochs on random crops from McKinley DEM
+- **Data Augmentation**: Random crops, rotations, flips, noise
+
+## Validation
+
+The notebook includes checks for:
+- Input data statistics and validity
+- Training fit (MAE/RMSE on validation crops)
+- Output alignment and correlation with SRTM trend
+
+## Citation
+
+If you use this work, please cite the original DeepDEM paper and datasets used.
+
+## License
+
+[Add your license here]</content>
+<parameter name="filePath">/home/besudo/Git/deepdem/DEM_SuperRes/README.md