Diff-DEM: A Diffusion Probabilistic Approach to Digital Elevation Model Void Filling

Setup

conda env create -f environment.yml
conda activate Diff-DEM

Download Dataset & Pretrained Model

Download here .

Unzip and place dataset under the Diff-DEM/dataset of repo e.g. Diff-DEM/dataset/norway_dem

Place Diff-DEM pretrained model at Diff-DEM/pretrained/760_Network.pth

The results of generative_model, spline, void_fill tested on Gavriil's dataset are sourced from repo.

Training

python run.py -p train -c config/dem_completion.json

See training progress

tensorboard --logdir experiments/train_dem_completion_XXXXXX_XXXXXX

Inference

python run.py -p test -c config/dem_completion.json \
    --resume ./pretrained/760 \
    --n_timestep 512 \
    --data_root ./dataset/norway_dem/benchmark/benchmark_gt.flist \
    --mask_root ./dataset/norway_dem/benchmark/mask_64-96.flist

Tested on NVIDIA RTX3090. Please adjust batch_size in JSON file if out of GPU memory.

Metric

Evaluate the predicted DEM. For example:

python data/util/tif_metric.py \
    --gt_tif_dir ./dataset/norway_dem/benchmark/gt \
    --mask_dir ./dataset/norway_dem/benchmark/mask/128-160 \
    --algo_dir ./experiments/Diff-DEM/128-160/results/test/0 \
    --normalize

Set --algo_dir to the DEMs predicted by model e.g. experiments/test_dem_completion_XXXXXX_XXXXXX/results/test/0
For Diff-DEM generated results, use --normalize, otherwise do not use.

Visualization

We view the uint16 DEMs using ImageJ

Acknowledge

This project is based on the following wonderful implementation of the paper Palette: Image-to-Image Diffusion Models
https://github.com/Janspiry/Palette-Image-to-Image-Diffusion-Models

Also, Gavriil's dataset provided in dem-fill.

Lastly, our complete Norway dataset are curated from Norwegian Mapping Authority.

Citing

@article{lo2024diff,
  title={Diff-DEM: A Diffusion Probabilistic Approach to Digital Elevation Model Void Filling},
  author={Lo, Kyle Shih-Huang and Peters, J{\"o}rg},
  journal={IEEE Geoscience and Remote Sensing Letters},
  year={2024},
  publisher={IEEE}
}