Visualizing the Consequences of Climate Change Using Cycle-Consistent Adversarial Networks
This work addresses the challenge of communicating climate change risks to the general public by providing personalized visualizations, though it is incremental as it applies an existing method to a new domain.
The paper tackles the problem of visualizing climate change impacts by using CycleGANs to generate images of houses after extreme weather events, based on street-view images and climate model predictions for 50-year forecasts, aiming to make future risks more tangible.
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 viewers 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.