Kuro Siwo：33十亿平方米水下区域。一个全球多时相卫星数据集，用于快速洪水制图
Kuro Siwo: 33 billion $m^2$ under the water. A global multi-temporal satellite dataset for rapid flood mapping
AI总结：
Kuro Siwo是一个全球多时相卫星数据集，用于快速洪水制图，包含33十亿平方米的洪水和永久水域数据，并提供优化的SAR数据产品和基准测试。
摘要：
全球洪水，加剧于气候变化，对人类生命、基础设施和环境构成严重威胁。最近巴基斯坦和新西兰的灾难性事件凸显了精确洪水制图的紧迫性，以指导恢复工作，理解脆弱性，并为未来事件做准备。虽然合成孔径雷达（SAR）遥感技术具有日间和夜间、全天候成像能力，但其在深度学习中用于洪水分割的应用受到缺乏大规模标注数据集的限制。为此，我们引入了Kuro Siwo，一个人工标注的多时相数据集，覆盖全球43个洪水事件。我们的数据集映射超过338十亿平方米的土地，其中33十亿被指定为洪水区域或永久水域。Kuro Siwo包括一个高度处理过的产品，专门用于基于SAR地面范围探测的洪水制图，以及一个原始SAR单视复数产品，经过最小预处理，旨在促进对相位和幅度信息的利用研究，并为下游任务预处理提供最大灵活性。为了利用大规模自监督预训练方法在遥感数据中的进展，我们通过大量未标记的SAR样本增强Kuro Siwo。最后，我们提供了一个广泛的基准，即BlackBench，为来自欧洲、美洲、非洲、亚洲和澳大利亚的多样洪水事件提供强大的基线。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
