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--- |
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license: mit |
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tags: |
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- pytorch |
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- pytorch-lightning |
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- dem |
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- super-resolution |
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- remote-sensing |
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- geospatial |
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--- |
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# DEM Super-Resolution |
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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. |
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## Overview |
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The implementation uses an adapted DeepDEM model with a U-Net architecture (ResNet34 encoder) that takes 7 input channels: |
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- SRTM DEM (30m) |
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- Sentinel-2 RGB bands (10m) |
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- Sentinel-2 NIR band (10m) |
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- NDVI |
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- Nodata mask |
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The model predicts residual corrections to be added to a smoothed SRTM trend, producing 10m synthetic DEMs. |
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## Requirements |
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- Python 3.8+ |
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- PyTorch 2.0+ |
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- PyTorch Lightning |
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- Segmentation Models PyTorch |
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- Rasterio |
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- Geopandas |
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- Albumentations |
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- Earth Engine API (for data acquisition) |
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- GDAL |
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- Boto3 (for LiDAR data download) |
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## Installation |
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1. Clone the repository: |
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```bash |
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git clone https://github.com/nfl0/DEM_SuperRes.git |
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cd DEM_SuperRes |
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``` |
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2. Install dependencies: |
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```bash |
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pip install torch torchvision torchaudio pytorch-lightning segmentation-models-pytorch rasterio geopandas albumentations scipy gdown earthengine-api boto3 |
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``` |
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3. Install system dependencies: |
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```bash |
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apt-get install libspatialindex-dev libgdal-dev gdal-bin |
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pip install gdal |
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``` |
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## Data Acquisition |
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The notebook handles data acquisition from: |
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- **SRTM 30m DEM**: CGIAR/SRTM90_V4 via Google Earth Engine |
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- **Sentinel-2 10m imagery**: COPERNICUS/S2_SR_HARMONIZED via Google Earth Engine |
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- **High-resolution LiDAR DEM**: OpenTopography (McKinley Mine, NM) |
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Authenticate with Google Earth Engine and ensure access to required datasets. |
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## Usage |
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1. Open `DEM_SuperRes.ipynb` in Google Colab or Jupyter. |
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2. Run cells sequentially to: |
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- Acquire and preprocess training data (McKinley) |
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- Train the model |
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- Acquire and preprocess inference data (Marrakech) |
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- Generate synthetic DEM |
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- Run validation checks |
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3. Key outputs: |
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- Trained model: `Models/deepdem_model.ckpt` |
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- Synthetic DEM: `synth_dem_marrakech.tif` |
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## Model Training |
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- **Architecture**: U-Net with ResNet34 encoder, 7 input channels, 1 output channel (residuals) |
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- **Loss**: L1 loss |
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- **Optimizer**: Adam (lr=1e-4) |
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- **Training**: 5 epochs on random crops from McKinley DEM |
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- **Data Augmentation**: Random crops, rotations, flips, noise |
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## Validation |
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The notebook includes checks for: |
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- Input data statistics and validity |
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- Training fit (MAE/RMSE on validation crops) |
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- Output alignment and correlation with SRTM trend |
