Towards Tracking the Emissions of Every Power Plant on the Planet

Towards Tracking the Emissions of Every Power Plant on the Planet by Couture et al. (2020) won the Best Pathway to Impact award at the NeurIPS 2020 CCAI workshop. Supported by Al Gore’s Climate TRACE (and grants from Google.org and Bloomberg Philanthropies), the project “[uses] machine learning to infer power generation and emissions from visible and thermal power plant signatures in satellite images.” In this initial paper, the authors present models that can predict whether a power plant is currently on or off from a single satellite image; their best model, a convolutional neural network, gets a mean average precision of 81% on this binary classification task. Interestingly, they find that the “vapor plume” (steam) from a power plant’s cooling system is a better indicator for its emissions than the “smoke plume” (greenhouse gasses) coming out of its main chimney.