  Global 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 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 from Europe,
America, Africa, Asia and Australia.
