Visualizing the Consequences of Climate Change Using Cycle-Consistent Adversarial Networks
By Victor Schmidt, Alexandra Sasha Luccioni, S. Mukkavilli, Narmada M. Balasooriya, Kris Sankaran, Jennifer Tour Chayes, Yoshua Bengio. 2019
43 citations recorded by the source corpus.
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.
Subfields: Ecological Modeling, General Social Sciences, Sociology and Political Science, Generative Models
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Ecological Modeling
In General Social Sciences
In Sociology and Political Science
In Generative Models
- Alexandre Lacoste
- John C. Platt
- Yoshua Bengio
- Natasha Jaques
- Konrad Paul Kording
- Demis Hassabis
- Aaron C. Courville
- Stefano Ermon
Citations in the corpus
- Quantifying the Carbon Emissions of Machine Learning (2019, background): “On one hand, there is valuable research to be done in ML especially with regards to tackling climate change [25, 26], whereas on the other hand, the emissions of the field of ML are growing quickly [1].”
- Tackling Climate Change with Machine Learning (2019, methodology): “To make the effects of climate change more real for consumers, and thus help motivate those who wish to act, image generation techniques such as CycleGANs have been used to visualize the potential consequences of extreme weather events on houses and cities [702].”