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

Citation

If you use this work please cite:

@article{bountos2023kuro,
  title={Kuro Siwo: 12.1 billion $ m\^{} 2$ under the water. A global multi-temporal satellite dataset for rapid flood mapping},
  author={Bountos, Nikolaos Ioannis and Sdraka, Maria and Zavras, Angelos and Karasante, Ilektra and Karavias, Andreas and Herekakis, Themistocles and Thanasou, Angeliki and Michail, Dimitrios and Papoutsis, Ioannis},
  journal={arXiv preprint arXiv:2311.12056},
  year={2023}
}

Download Kuro Siwo

• The Kuro Siwo 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 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

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:

• FloodViT
• SNUNet