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Change","sub_category":"Natural Hazard and Storms","monthly_downloads":0,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"# [Kuro Siwo: A global multi-temporal SAR dataset for rapid flood mapping](https://arxiv.org/abs/2311.12056)\n\n  #### Latest updates:\n    - [✔️] Update codebase for KuroSiwo v2 + updated mean/stds\n    - [✔️] Updated citation \n    - [ ] TODO: Expand README with more elaborate guidelines\n    - [ ] TODO: Upload Kuro-Siwo to HuggingFace\n\n![Kuro Siwo](imgs/kuro_spatial.png)\n\n\n# Table of Contents\n- [Download the dataset](#download-kuro-siwo)\n- [Data preprocessing](#data-preprocessing)\n- [Repository structure](#kuro-siwo-repo-structure)\n- [Pretrained models](#pretrained-models)\n- [Citation](#citation)\n- [License](#license)\n\n\n### Download Kuro Siwo\n\n  #### GRD Data\n- The Kuro Siwo GRD Dataset can be downloaded either:\n  - from the following [link](https://www.dropbox.com/scl/fo/xc69aclh0q4lykd22ynkb/AAaDu8gBtoSdOpmffv7JY50?rlkey=uds2b2aot6oubc9hmnrm7myy7\u0026st=21u41kwx\u0026dl=0),\n\n\n  - or by executing ```scripts/download_kuro_siwo.sh```. This script will download and prepare the Kuro Siwo GRDD dataset for deep learning.\n\n    #### Usage \n\n    1. Make sure to grant the necessary rights by executing `chmod +x scripts/download_kuro_siwo.sh`\n    2. Execute `scripts/download_kuro_siwo.sh DESIRED_DATASET_ROOT_PATH` e.g: `./download_kuro_siwo.sh KuroRoot`\n\n\n#### SLC Data\n  - The SLC Preprocessed products can be downloaded from the following [link](https://www.dropbox.com/scl/fo/kknf6ycz6ywffopjxroys/AOIedl2NgWnOXQBEDUGv4m0?rlkey=rb18w8rzpwitg2w3nlhzklnyy\u0026st=p1vv516h\u0026dl=0).\n\n  - Similarly, the cropped SLC patches (224x224 pixels) can be acquired from the following [link](https://www.dropbox.com/scl/fo/6u1bhbhd34rnn0u47o8dj/AK9vblAzDWqhPTqYvioPUb8?rlkey=i7k862563n936akuqlsdf3w66\u0026st=0f7q3vno\u0026dl=0).  \n\n\n### Data preprocessing\n\nThe preprocessing pipelines used to generate the GRD and SLC products can be found at `configs/grd_preprocessing.xml` and `configs/slc_preprocessing.xml` repsectively.\n\n\n### Kuro Siwo repo structure \n  - Kuro Siwo uses the [black](https://github.com/psf/black) python formatter. To activate it install pre-commit, running `pip install pre-commit`\nand execute `pre-commit install`.\n  - Training starts by running `python main.py`. The configurations are defined in the `configs` directory\n e.g \n    - model,\n    - training pipeline \n      - Segmentation,\n      - change detection\n    - hyperparameters\n  - `main.py` supports command line arguments that override the config files.\n     e.g \n      ```\n         python main.py --method=unet --backbone=resnet18 --dem=True --slope=False --batch_size=32\n      ```\n\n\n### Pretrained models\nThe weights of the top performing models can be accessed using the following links:\n  - [FloodViT](https://www.dropbox.com/scl/fi/srw7u4cw1gtxrf4xzmsh7/floodvit.pt?rlkey=snskpq1qrdav5u2jya8k2bocg\u0026dl=0)\n  - [SNUNet](https://www.dropbox.com/scl/fi/3vlsveoobqe1wc71s5z2d/best_segmentation.pt?rlkey=xpy2thmozzxfzymr8b13m7n51\u0026dl=0)\n\n\n### Citation\nIf you use this work please cite:\n```\n@inproceedings{NEURIPS2024_43612b06,\n 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},\n booktitle = {Advances in Neural Information Processing Systems},\n editor = {A. Globerson and L. Mackey and D. Belgrave and A. Fan and U. Paquet and J. Tomczak and C. Zhang},\n pages = {38105--38121},\n publisher = {Curran Associates, Inc.},\n title = {Kuro Siwo: 33 billion m\\^{}2 under the water. A global multi-temporal satellite dataset for rapid flood mapping},\n url = {https://proceedings.neurips.cc/paper_files/paper/2024/file/43612b0662cb6a4986edf859fd6ebafe-Paper-Datasets_and_Benchmarks_Track.pdf},\n volume = {37},\n year = {2024}\n}\n```\n### License\nThe Kuro Siwo dataset is released under the CC BY license.\n","funding_links":[],"readme_doi_urls":[],"works":{},"citation_counts":{},"total_citations":0,"keywords_from_contributors":[],"project_url":"https://ost.ecosyste.ms/api/v1/projects/148847","html_url":"https://ost.ecosyste.ms/projects/148847"}