FloodNet-to-FloodGAN : Generating Flood Scenes in Aerial Images
Résumé
A global rise in the occurrences of natural disasters and human-borne conflicts has put a spotlight on the need for Earth Observation (EO) data in designing practical Humanitarian Assistance and Disaster Relief (HADR) interventions. Novel techniques that leverage remotely sensed data are leading to a paradigm shift in our understanding of such situations and improving the efficacy of our response. Aerial flood maps can provide localized insight into the extent of flood-related damage and the degree to which communities' access to shelter, clean water, and communication channels have been compromised. Unfortunately, such insights typically only emerge hours or days after a flooding event has occurred. Moreover, a dearth of available historical data restricts the development of practical machine learning based methods. This work examines the use of Generative Adversarial Networks (GANs) in simulating flooding in aerial images. We first introduce the Houston UAV dataset, an extension of the FloodNet dataset. Our dataset accommodates more well-defined semantic classes and significantly reduces the label noise in semantic masks. We propose a GAN-based pipeline to generate flood conditions in non-flooded regions, generating synthetic flooding scenes for predictive mapping. Code and dataset are available at https://github.com/granularai/flood-synthesis.
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