mlsanityData to 7 Oct 2026

ClimateGAN: Raising Climate Change Awareness by Generating Images of Floods

arXiv 2110.02871 ↗First posted 6 Oct 2021cs.CV cs.AI cs.CYcited by 14

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Abstract (arXiv)

Climate change is a major threat to humanity, and the actions required to prevent its catastrophic consequences include changes in both policy-making and individual behaviour. However, taking action requires understanding the effects of climate change, even though they may seem abstract and distant. Projecting the potential consequences of extreme climate events such as flooding in familiar places can help make the abstract impacts of climate change more concrete and encourage action. As part of a larger initiative to build a website that projects extreme climate events onto user-chosen photos, we present our solution to simulate photo-realistic floods on authentic images. To address this complex task in the absence of suitable training data, we propose ClimateGAN, a model that leverages both simulated and real data for unsupervised domain adaptation and conditional image generation. In this paper, we describe the details of our framework, thoroughly evaluate components of our architecture and demonstrate that our model is capable of robustly generating photo-realistic flooding.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 38 of its 65 references that are in the map. First posted on arXiv 6 Oct 2021.

Builds on

38 of 65 references.

References · 38

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26 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 2; 2024: 0; 2025: 5; 2026: 5.

14 papers.

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