Towards Tracking the Emissions of Every Power Plant on the Planet (Papers Track) Best Pathway to Impact
Heather D Couture (Pixel Scientia Labs); Joseph O'Connor (Carbon Tracker); Grace Mitchell (WattTime); Isabella Söldner-Rembold (Carbon Tracker); Durand D'souza (Carbon Tracker); Krishna Karra (WattTime); Keto Zhang (WattTime); Ali Rouzbeh Kargar (WattTime); Thomas Kassel (WattTime); Brian Goldman (Google); Daniel Tyrrell (Google); Wanda Czerwinski (Google); Alok Talekar (Google); Colin McCormick (Georgetown University)
Greenhouse gases emitted from fossil-fuel-burning power plants are a major contributor to climate change. Current methods to track emissions from individual sources are expensive and only used in a few countries. While carbon dioxide concentrations can be measured globally using remote sensing, background fluctuations and low spatial resolution make it difficult to attribute emissions to individual sources. We use machine learning to infer power generation and emissions from visible and thermal power plant signatures in satellite images. By training on a data set of power plants for which we know the generation or emissions, we are able to apply our models globally. This paper demonstrates initial progress on this project by predicting whether a power plant is on or off from a single satellite image.