Accurate detection of inundated water extents during flooding events is
crucial in emergency response decisions and aids in recovery efforts. Satellite
Remote Sensing data provides a global framework for detecting flooding extents.
Specifically, Sentinel-1 C-Band Synthetic Aperture Radar (SAR) imagery has
proven to be useful in detecting water bodies due to low backscatter of water
features in both co-polarized and cross-polarized SAR imagery. However,
increased backscatter can be observed in certain flooded regions such as
presence of infrastructure and trees - rendering simple methods such as pixel
intensity thresholding and time-series differencing inadequate. Machine
Learning techniques has been leveraged to precisely capture flood extents in
flooded areas with bumps in backscatter but needs high amounts of labelled data
to work desirably. Hence, we created a labeled known water body extent and
flooded area extents during known flooding events covering about 36,000 sq.
kilometers of regions within mainland U.S and Bangladesh. Further, We also
leveraged citizen science by open-sourcing the dataset and hosting an open
competition based on the dataset to rapidly prototype flood extent detection
using community generated models. In this paper we present the information
about the dataset, the data processing pipeline, a baseline model and the
details about the competition, along with discussion on winning approaches. We
believe the dataset adds to already existing datasets based on Sentinel-1C SAR
data and leads to more robust modeling of flood extents. We also hope the
results from the competition pushes the research in flood extent detection
further.

洪水事件中准确检测淹没范围对紧急响应决策和恢复工作至关重要。卫星遥感数据提供了一个全球框架，可以检测洪水范围。具体来说，哨兵 - 1 C 波段合成孔径雷达（SAR）图像已被证明对于检测水体非常有用，因为水体在同极化和交叉极化 SAR 图像中的反射较低。然而，在某些洪水区域，如基础设施和树木的存在，会观察到反射增加，从而使像素强度阈值和时间序列差异等简单方法无法胜任任务。机器学习技术已被利用来精确捕捉具有反射增加的洪水区域的洪水范围，但需要大量标记数据以达到理想的效果。因此，我们创建了一个包含美国本土和孟加拉国境内约 36,000 平方公里地区在已知洪水事件中水体范围和淹没区域范围的标记数据集。此外，我们还通过开源数据集并基于该数据集举办了一个开放竞赛，以便利用社区生成的模型快速原型化洪水范围检测。在本文中，我们介绍了该数据集的信息，数据处理流程，一个基线模型以及竞赛的细节，并讨论了获胜方法。我们相信该数据集将增添已有的基于 Sentinel-1C SAR 数据的数据集，并推动洪水范围检测的研究。