Kuro Siwo: 33 billion $m^2$ under the water. A global multi-temporal satellite dataset for rapid flood mapping

Nikolaos Ioannis Bountos, Maria Sdraka, Angelos Zavras, Andreas Karavias, Ilektra Karasante, Themistocles Herekakis, Angeliki Thanasou, Dimitrios Michail, Ioannis Papoutsis

Keywords: Synthetic Aperture Radar, SAR, Deep Learning, Remote Sensing, Floods, dataset

TL;DR: A global multi-temporal SAR dataset for rapid flood mapping

Abstract:

Global flash floods, exacerbated by climate change, pose severe threats to human
life, infrastructure, and the environment. Recent catastrophic events in Pakistan and
New Zealand underscore the urgent need for precise flood mapping to guide restoration efforts, understand vulnerabilities, and prepare for future occurrences. While Synthetic Aperture Radar (SAR) remote sensing offers day-and-night, all-weather
imaging capabilities, its application in deep learning for flood segmentation is limited by the lack of large annotated datasets. To address this, we introduce Kuro
Siwo, a manually annotated multi-temporal dataset, spanning 43 flood events globally. Our dataset maps more than 338 billion $m^2$ of land, with 33 billion designated
as either flooded areas or permanent water bodies. Kuro Siwo includes a highly
processed product optimized for flash flood mapping based on SAR Ground Range
Detected, and a primal SAR Single Look Complex product with minimal preprocessing, designed to promote research on the exploitation of both the phase and amplitude information and to offer maximum flexibility for downstream task preprocessing. To leverage advances in large scale self-supervised pretraining methods
for remote sensing data, we augment Kuro Siwo with a large unlabeled set of SAR
samples. Finally, we provide an extensive benchmark, namely BlackBench, offering strong baselines for a diverse set of flood events globally. All data and code are
published in our Github repository: https://github.com/Orion-AI-Lab/KuroSiwo.

Submission Number: 1883