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[2010.08103] Physics-informed GANs for Coastal Flood Visualization

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As climate change increases the intensity of natural disasters, society needs better tools for adaptation. Floods, for example, are the most frequent natural disaster, but during hurricanes the area is largely covered by clouds and emergency managers must rely on nonintuitive flood visualizations for mission planning. To assist these emergency managers, we have created a deep learning pipeline that generates visual satellite images of current and future coastal flooding. We advanced a state-of-the-art GAN called pix2pixHD, such that it produces imagery that is physically-consistent with the output of an expert-validated storm surge model (NOAA SLOSH). By evaluating the imagery relative to physics-based flood maps, we find that our proposed framework outperforms baseline models in both physical-consistency and photorealism. While this work focused on the visualization of coastal floods, we envision the creation of a global visualization of how climate change will shape our earth.

[2010.08103] Physics-informed GANs for Coastal Flood Visualization Skip to main content arXiv is now an independent nonprofit! Learn more × Search arXiv Press Enter to search · Advanced search --> Computer Science > Computer Vision and Pattern Recognition arXiv:2010.08103 (cs) [Submitted on 16 Oct 2020 ( v1 ), last revised 12 Feb 2021 (this version, v2)] Title: Physics-informed GANs for Coastal Flood Visualization Authors: Björn Lütjens , Brandon Leshchinskiy , Christian Requena-Mesa , Farrukh Chishtie , Natalia Díaz-Rodriguez , Océane Boulais , Aaron Piña , Dava Newman , Alexande

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