  Accurate and timely mapping of flood extent from high-resolution satellite
imagery plays a crucial role in disaster management such as damage assessment
and relief activities. However, current state-of-the-art solutions are based on
U-Net, which can-not segment the flood pixels accurately due to the ambiguous
pixels (e.g., tree canopies, clouds) that prevent a direct judgement from only
the spectral features. Thanks to the digital elevation model (DEM) data readily
available from sources such as United States Geological Survey (USGS), this
work explores the use of an elevation map to improve flood extent mapping. We
propose, EvaNet, an elevation-guided segmentation model based on the
encoder-decoder architecture with two novel techniques: (1) a loss function
encoding the physical law of gravity that if a location is flooded (resp. dry),
then its adjacent locations with a lower (resp. higher) elevation must also be
flooded (resp. dry); (2) a new (de)convolution operation that integrates the
elevation map by a location sensitive gating mechanism to regulate how much
spectral features flow through adjacent layers. Extensive experiments show that
EvaNet significantly outperforms the U-Net baselines, and works as a perfect
drop-in replacement for U-Net in existing solutions to flood extent mapping.
