---
license: mit
tags:
- pytorch
- pytorch-lightning
- dem
- super-resolution
- remote-sensing
- geospatial
---

# 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/nfl0/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

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