2019/05/02 by Victor Schmidt, Alexandra Sasha Luccioni, Schmidt, Victor +12 · 1 voice · 15 citations
Computer Science · Earth and Planetary Sciences · Environmental Science · Social Sciences · #Adversarial system #Artificial intelligence #Climate Change Communication and Perception #Climate change #Climate model #Climatology #Computational and Text Analysis Methods #Computer science #Credibility #Data science #Ecology #Environmental resource management #Environmental science #Geography #Geology #Meteorological Phenomena and Simulations #Political science #Species Distribution and Climate Change #cs.AI #cs.CV
paper · pdf · doi:10.48550/arxiv.1905.03709
published in arXiv (Cornell University) (Cornell University)
arxiv created 2019/05/02 · openalex publication_date 2019/05/02 · arxiv updated 2019/05/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
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.