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

• DOI: 10.48550/arxiv.2311.12056
• Publication Date: 2023-01-01
• Authors: Bountos, Nikolaos...; Sdraka, Maria; Zavras, Angelos; Karasante, Ilektra; Karavias, Andreas; Herekakis, Themis...; Thanasou, Angeliki; Michail, Dimitrios; Papoutsis, Ioannis
• Keywords: I.2; FOS: Computer and information sciences; Computer Science - Machine Learning; I.4; Computer Science - Artificial Intelligence; I.5.4; Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV); Computer Science - Computer Vision and Pattern Recognition; 15. Life on land; Electrical Engineering and Systems Science - Image and Video Processing; 6. Clean water; Machine Learning (cs.LG); Artificial Intelligence (cs.AI); I.2; I.4; I.5.4; 13. Climate action; FOS: Electrical engineering, electronic engineering, information engineering

Accepted at the 38th Conference on Neural Information Processing Systems (NeurIPS 2024) Track on Datasets and Benchmarks<br/>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.<br/>