Physics-informed GANs for Coastal Flood Visualization

TL;DR

This paper introduces a physics-informed GAN framework that generates realistic satellite images of coastal floods, aiding emergency planning by providing physically consistent visualizations aligned with storm surge models.

Contribution

It advances a state-of-the-art GAN to produce physically consistent flood imagery based on expert-validated storm surge models, improving visualization accuracy.

Findings

Outperforms baseline models in physical consistency

Generates photorealistic flood images

Aligns with NOAA storm surge model outputs

Abstract

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.