The same generative adversarial network (GAN) technology that gave us time-lapse or shapeshifting selfie filters, is also being used for social good. In a study presented by Cornell University researchers, “Visualizing the Consequences of Climate Change Using Cycle-Consistent Adversarial Networks” uses image generation with CycleGANs technology to “depict accurate, vivid, and personalized outcomes of climate change.”
The goal of the 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.”
Here is a step-by-step of the researchers proposed project:
- Developing a Machine Learning (ML) based tool that utilizes probable effect to generate an image that is familiar to the user.
- The first iteration will focus on the devastation of flooding natural disasters and then use CycleGANs to generate a static timeframe and corresponding visual.
- The second iteration will incorporate other natural disasters, such as fires and droughts, varying time horizons, in addition to granting the viewer more decisions on the selection of climate change effects.
- The model uses domain-level mapping to simulate the before-and-after effects on a home or familiar image.
Challenges that are cited as accompanying this new ML technology, include “super-resolution, classification, climate down-scaling, forecasting, emulating simulations, localization, detection, and tracking of extreme events or anomalies.”
I read the article mentioned above (https://www.technologyreview.com/f/613547/ai-can-show-us-the-ravages-of-climate-change/) and thought it was interesting. While I am not offering an endorsement of a strategy, tactics, thoughts, service nor a company or
