https://github.com/Orion-AI-Lab/KuroSiwo
Code and data for Kuro Siwo flood mapping dataset
https://github.com/Orion-AI-Lab/KuroSiwo
computer-vision flood remote-sensing sar synthetic-aperture-radar
Last synced: 3 months ago
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Code and data for Kuro Siwo flood mapping dataset
- Host: GitHub
- URL: https://github.com/Orion-AI-Lab/KuroSiwo
- Owner: Orion-AI-Lab
- License: mit
- Created: 2023-11-18T11:09:25.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2025-04-03T08:24:04.000Z (7 months ago)
- Last Synced: 2025-07-19T17:31:34.994Z (3 months ago)
- Topics: computer-vision, flood, remote-sensing, sar, synthetic-aperture-radar
- Language: Python
- Homepage: https://orion-ai-lab.github.io/publication/bountos-2023-kuro/
- Size: 30.7 MB
- Stars: 63
- Watchers: 4
- Forks: 6
- Open Issues: 10
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- open-sustainable-technology - Kuro Siwo - A meticulously curated multi-temporal dataset spanning 32 global flood events, mapping over 63 billion areas of land. (Climate Change / Natural Hazard and Storm)
README
# [Kuro Siwo: A global multi-temporal SAR dataset for rapid flood mapping](https://arxiv.org/abs/2311.12056)
#### Latest updates:
- [✔️] Update codebase for KuroSiwo v2 + updated mean/stds
- [✔️] Updated citation
- [ ] TODO: Expand README with more elaborate guidelines
- [ ] TODO: Upload Kuro-Siwo to HuggingFace
# Table of Contents
- [Download the dataset](#download-kuro-siwo)
- [Data preprocessing](#data-preprocessing)
- [Repository structure](#kuro-siwo-repo-structure)
- [Pretrained models](#pretrained-models)
- [Citation](#citation)
### Download Kuro Siwo
#### GRD Data
- The Kuro Siwo GRD Dataset can be downloaded either:
- from the following [link](https://www.dropbox.com/scl/fo/xc69aclh0q4lykd22ynkb/AAaDu8gBtoSdOpmffv7JY50?rlkey=uds2b2aot6oubc9hmnrm7myy7&st=21u41kwx&dl=0),
- or by executing ```scripts/download_kuro_siwo.sh```. This script will download and prepare the Kuro Siwo GRDD dataset for deep learning.
#### Usage
1. Make sure to grant the necessary rights by executing `chmod +x scripts/download_kuro_siwo.sh`
2. Execute `scripts/download_kuro_siwo.sh DESIRED_DATASET_ROOT_PATH` e.g: `./download_kuro_siwo.sh KuroRoot`
#### SLC Data
- The SLC Preprocessed products can be downloaded from the following [link](https://www.dropbox.com/scl/fo/kknf6ycz6ywffopjxroys/AOIedl2NgWnOXQBEDUGv4m0?rlkey=rb18w8rzpwitg2w3nlhzklnyy&st=p1vv516h&dl=0).
- Similarly, the cropped SLC patches (224x224 pixels) can be acquired from the following [link](https://www.dropbox.com/scl/fo/6u1bhbhd34rnn0u47o8dj/AK9vblAzDWqhPTqYvioPUb8?rlkey=i7k862563n936akuqlsdf3w66&st=0f7q3vno&dl=0).
### Data preprocessing
The preprocessing pipelines used to generate the GRD and SLC products can be found at `configs/grd_preprocessing.xml` and `configs/slc_preprocessing.xml` repsectively.
### Kuro Siwo repo structure
- Kuro Siwo uses the [black](https://github.com/psf/black) python formatter. To activate it install pre-commit, running `pip install pre-commit`
and execute `pre-commit install`.
- Training starts by running `python main.py`. The configurations are defined in the `configs` directory
e.g
- model,
- training pipeline
- Segmentation,
- change detection
- hyperparameters
- `main.py` supports command line arguments that override the config files.
e.g
```
python main.py --method=unet --backbone=resnet18 --dem=True --slope=False --batch_size=32
```
### Pretrained models
The weights of the top performing models can be accessed using the following links:
- [FloodViT](https://www.dropbox.com/scl/fi/srw7u4cw1gtxrf4xzmsh7/floodvit.pt?rlkey=snskpq1qrdav5u2jya8k2bocg&dl=0)
- [SNUNet](https://www.dropbox.com/scl/fi/3vlsveoobqe1wc71s5z2d/best_segmentation.pt?rlkey=xpy2thmozzxfzymr8b13m7n51&dl=0)
### Citation
If you use this work please cite:
```
@inproceedings{NEURIPS2024_43612b06,
author = {Bountos, Nikolaos Ioannis and Sdraka, Maria and Zavras, Angelos and Karavias, Andreas and Karasante, Ilektra and Herekakis, Themistocles and Thanasou, Angeliki and Michail, Dimitrios and Papoutsis, Ioannis},
booktitle = {Advances in Neural Information Processing Systems},
editor = {A. Globerson and L. Mackey and D. Belgrave and A. Fan and U. Paquet and J. Tomczak and C. Zhang},
pages = {38105--38121},
publisher = {Curran Associates, Inc.},
title = {Kuro Siwo: 33 billion m\^{}2 under the water. A global multi-temporal satellite dataset for rapid flood mapping},
url = {https://proceedings.neurips.cc/paper_files/paper/2024/file/43612b0662cb6a4986edf859fd6ebafe-Paper-Datasets_and_Benchmarks_Track.pdf},
volume = {37},
year = {2024}
}
```