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

arXiv: https://arxiv.org/abs/1905.03709 · HTML: https://mlsanity.com/paper/1905.03709/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2019.

## Built on by (most cited first, 12 of 12)

- [Breaking the Dilemma of Medical Image-to-image Translation](https://mlsanity.com/paper/2110.06465/) (2021)
- [ClimateNeRF: Extreme Weather Synthesis in Neural Radiance Field](https://mlsanity.com/paper/2211.13226/) (2022)
- [Incidents1M: a large-scale dataset of images with natural disasters, damage, and incidents](https://mlsanity.com/paper/2201.04236/) (2022)
- [ClimateGAN: Raising Climate Change Awareness by Generating Images of Floods](https://mlsanity.com/paper/2110.02871/) (2021)
- [Generating Physically-Consistent Satellite Imagery for Climate Visualizations](https://mlsanity.com/paper/2104.04785/) (2021)
- [RainyGS: Efficient Rain Synthesis with Physically-Based Gaussian Splatting](https://mlsanity.com/paper/2503.21442/) (2025)
- [WeatherEdit: Controllable Weather Editing with 4D Gaussian Field](https://mlsanity.com/paper/2505.20471/) (2025)
- [Controllable Weather Synthesis and Removal with Video Diffusion Models](https://mlsanity.com/paper/2505.00704/) (2025)
- [Text-to-Image Generative AI for Modeling and Simulation: Methods, Opportunities, and Applications](https://mlsanity.com/paper/2606.20991/) (2026)
- [Re-designing cities with conditional adversarial networks](https://mlsanity.com/paper/2104.04013/) (2021)
- [Implementing and Experimenting with Diffusion Models for Text-to-Image Generation](https://mlsanity.com/paper/2209.10948/) (2022)
- [Generative models for visualising abstract social processes: Guiding streetview image synthesis of StyleGAN2 with indices of deprivation](https://mlsanity.com/paper/2312.00570/) (2023)

## Abstract (arXiv)

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.