  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.
