Global floods, exacerbated by climate change, pose severe threats to human
life, infrastructure, and the environment. This urgency is highlighted by
recent catastrophic events in Pakistan and New Zealand, underlining the
critical need for precise flood mapping for guiding restoration efforts,
understanding vulnerabilities, and preparing for future events. While Synthetic
Aperture Radar (SAR) offers day-and-night, all-weather imaging capabilities,
harnessing it for deep learning is hindered by the absence of a large annotated
dataset. To bridge this gap, we introduce Kuro Siwo, a meticulously curated
multi-temporal dataset, spanning 32 flood events globally. Our dataset maps
more than 63 billion m2 of land, with 12.1 billion of them being either a
flooded area or a permanent water body. Kuro Siwo stands out for its
unparalleled annotation quality to facilitate rapid flood mapping in a
supervised setting. We also augment learning by including a large unlabeled set
of SAR samples, aimed at self-supervised pretraining. We provide an extensive
benchmark and strong baselines for a diverse set of flood events from Europe,
America, Africa and Australia. Our benchmark demonstrates the quality of Kuro
Siwo annotations, training models that can achieve $\approx$ 85% and $\approx$
87% in F1-score for flooded areas and general water detection respectively.
This work calls on the deep learning community to develop solution-driven
algorithms for rapid flood mapping, with the potential to aid civil protection
and humanitarian agencies amid climate change challenges. Our code and data
will be made available at this https URL

全球洪水、气候变化、洪水测绘、合成孔径雷达（SAR）和 Kuro Siwo 数据集与标注的广泛使用在深度学习中提供了实时洪水测绘的导向。