ClimateGAN: Raising Climate Change Awareness by Generating Images of Floods

Victor Schmidt, Alexandra Luccioni, Mélisande Teng, Tianyu Zhang, Alexia Reynaud, Sunand Raghupathi, Gautier Cosne, Adrien Juraver, Vahe Vardanyan, Alex Hernández-García, Yoshua Bengio

Keywords: GAN, Climate Change, Domain Adaptation, Representation Learning, Computer Vision, Application

Abstract: Climate change is a major threat to humanity and the actions required to prevent its catastrophic consequences include changes in both policy-making and individual behaviour. However, taking action requires understanding its seemingly abstract and distant consequences. Projecting the potential impacts of extreme climate events such as flooding in familiar places can help make the impacts of climate change more concrete and encourage action. As part of a larger initiative to build a website (https://thisclimatedoesnotexist.com) that projects extreme climate events onto user-chosen photos, we present our solution to simulate photo-realistic floods on authentic images. To address this complex task in the absence of suitable data, we propose ClimateGAN, a model that leverages both simulated and real data through unsupervised domain adaptation and conditional image generation. In this paper, we describe the details of our framework, thoroughly evaluate the main components of our architecture and demonstrate that our model is capable of robustly generating photo-realistic flooding on street images.

One-sentence Summary: This paper presents a model to robustly produce photo-realistic images of floods for raising climate change awareness, leveraging unsupervised domain adaptation and conditional image generation.