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Visualizing the Consequences of Climate Change Using Cycle-Consistent Adversarial Networks

Victor Schmidt, Alexandra Luccioni, S. Karthik Mukkavilli, Kris Sankaran, Yoshua Bengio, Narmada Balasooriya, Jennifer Chayes


We present a project that aims to generate images that depict accurate, vivid, and personalized outcomes of climate change using Cycle-Consistent Adversarial Networks (CycleGANs). By training our CycleGAN model on street-view images of houses before and after extreme weather events (e.g. floods, forest fires, etc.), we learn a mapping that can then be applied to images of locations that have not yet experienced these events. This visual transformation is paired with climate model predictions to assess likelihood and type of climate-related events in the long term (50 years) in order to bring the future closer in the viewer’s mind. The eventual goal of our project is to enable individuals to make more informed choices about their climate future by creating a more visceral understanding of the effects of climate change, while maintaining scientific credibility by drawing on climate model projections.

International Conference on Learning Representations (ICLR) 2019 AI for Social Good Workshop [WorkshopNat GeoPackt]