构建全球城市洪涝淹没数据集，助力卫星图像精准识别洪水范围。
Urban Flood Observations: A hand-labeled training and validation dataset of post-flood inundation
- 人工标注14场洪水的215张卫星图像，分辨率达3米。
- 模型在留一事件交叉验证下平均交并比达77.3%。
- 适合遥感、灾害监测与深度学习研究者使用。
城市洪水在全球范围内影响生命与基础设施。由于空间分辨率有限、获取频率低及云层遮挡，从卫星影像中映射复杂城市环境下的淹没范围仍具挑战。本文提出城市洪涝观测（Urban Flood Observations, UFO）数据集，涵盖2017至2021年间14场洪水事件的全球手标数据，共215个图像块（1024×1024像素），基于3米分辨率的PlanetScope影像。每个图像块标注两类：'淹没区'（所有可见地表水，包括洪水和永久/季节性水体）与'非淹没区'。为验证数据集价值，我们采用留一事件交叉验证训练分割模型，获得77.3%的平均交并比（IoU）。同时，用UFO评估两种主流地表水产品：基于Sentinel-1的NASA IMPACT模型与谷歌10米动态世界水体分类，分别取得44.1%和48.1%的IoU。UFO已公开，可支持城市淹没映射方法的研发与验证。
原文摘要 · Abstract (English)
Urban flooding affects lives and infrastructure worldwide. Mapping inundation in complex urban environments from satellite imagery remains challenging due to limited spatial resolution, infrequent acquisitions, and cloud cover. We present Urban Flood Observations (UFO), a global, hand-labeled dataset of post-flood inundation in diverse urban settings. UFO comprises 215 image chips (1024 by 1024 pixels) from 14 flood events between 2017 and 2021, derived from 3 m PlanetScope imagery. Each chip is annotated with two classes: 'inundated' (all visible surface water, including floodwater and pre-existing water bodies (permanent or seasonal)) and 'non-inundated'. To demonstrate the dataset's utility, we trained a segmentation model using leave-one-event-out cross-validation, achieving a mean Intersection over Union (IoU) of 77.3. We also used UFO to evaluate two widely used surface water products, the Sentinel-1-based NASA IMPACT model and Google's 10 m Dynamic World water class, which yielded IoUs of 44.1 and 48.1, respectively. UFO is publicly available to support the development and validation of urban inundation mapping methods.
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