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Open Sustainable Technology

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Kuro Siwo

A meticulously curated multi-temporal dataset spanning 32 global flood events, mapping over 63 billion areas of land.
https://github.com/orion-ai-lab/kurosiwo

Category: Climate Change
Sub Category: Natural Hazard and Storm

Keywords

computer-vision flood remote-sensing sar synthetic-aperture-radar

Last synced: about 18 hours ago
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Repository metadata

Code and data for Kuro Siwo flood mapping dataset

README.md

Kuro Siwo: A global multi-temporal SAR dataset for rapid flood mapping

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

Kuro Siwo

Table of Contents

Download Kuro Siwo

GRD Data

  • The Kuro Siwo GRD Dataset can be downloaded either:

    • from the following link,

    • 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.

  • Similarly, the cropped SLC patches (224x224 pixels) can be acquired from the following link.

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 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:

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}
}

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