https://github.com/prs-eth/bayes-forest-structure
https://github.com/prs-eth/bayes-forest-structure
Last synced: over 1 year ago
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- Host: GitHub
- URL: https://github.com/prs-eth/bayes-forest-structure
- Owner: prs-eth
- Created: 2021-11-25T16:41:45.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2023-09-28T08:37:15.000Z (almost 3 years ago)
- Last Synced: 2024-08-04T03:11:12.660Z (almost 2 years ago)
- Language: Python
- Size: 195 KB
- Stars: 18
- Watchers: 2
- Forks: 4
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
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README
# bayes-forest-structure
Code accompanying our paper "Country-wide Retrieval of Forest Structure From Optical and SAR Satellite Imagery With Bayesian Deep Learning" [[link to paper]](https://www.sciencedirect.com/science/article/pii/S0924271622003045)
## Code organization
* `./config` configuration files for data preprocessing, training and testing
* `./src` main project code
* `./src/scripts` helper scripts for preprocessing, evaluation etc.
## Getting started
### Prerequisites
* `python >= 3.8`
* `numpy >= 1.19`
* `pytorch >= 1.9`
* `scikit-image >= 0.18`
* `rasterio >= 1.1.5`
* `gdal >= 3.0`
* `fiona >= 1.8.13`
* `latextable`
### Inference
To apply our model to your data, preprocess the SAR data using the `/src/scripts/preprocess_s1.py` script and then reproject it to the Sentinel-2 tile. Then use `/src/scripts/predict_tile.py` to process the tile, passing the locations to the optical and SAR files as arguments. Make sure to also pass the paths of the `config.yaml` file (containing network configurations and statistics for data normalization) and of all five network checkpoints via the respective command line args. The files can be downloaded from [here](https://drive.google.com/drive/folders/1yUQOjBcbplg6I_9HmFp1vW8pdf4vOzxJ?usp=sharing). The output tiles will be written to the location passed as `--out_dir`.
### Training
For improved efficiency during training, the preprocessed and reprojected Sentinel images as well as the ground truth should be pickled first using the `/src/scripts/prepare_dataset.py` script. For training the `/src/train.py` script (along with the `/config/resnext.yaml` config file) is used.
## Citation
```
@article{becker2023country,
title={Country-wide retrieval of forest structure from optical and SAR satellite imagery with deep ensembles},
author={Becker, Alexander and Russo, Stefania and Puliti, Stefano and Lang, Nico and Schindler, Konrad and Wegner, Jan Dirk},
journal={ISPRS Journal of Photogrammetry and Remote Sensing},
volume={195},
pages={269--286},
year={2023},
publisher={Elsevier}
}
```