ClimateGAN: Raising Climate Change Awareness by Generating Images of Floods
This work addresses the challenge of raising public awareness about climate change by visualizing its effects, though it is incremental in applying existing domain adaptation and image generation techniques to a specific domain.
The authors tackled the problem of making climate change impacts more tangible by generating realistic flood images on user-provided photos, achieving photo-realistic results through a model that combines simulated and real data.
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 the effects of climate change, even though they may seem abstract and distant. Projecting the potential consequences of extreme climate events such as flooding in familiar places can help make the abstract impacts of climate change more concrete and encourage action. As part of a larger initiative to build a website 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 training data, we propose ClimateGAN, a model that leverages both simulated and real data for unsupervised domain adaptation and conditional image generation. In this paper, we describe the details of our framework, thoroughly evaluate components of our architecture and demonstrate that our model is capable of robustly generating photo-realistic flooding.