  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.
