GainForest: Scaling Climate Finance for Forest Conservation using Interpretable Machine Learning on Satellite Imagery (Ideas Track)
David Dao (ETH); Ce Zhang (ETH); Nick Beglinger (Cleantech21); Catherine Cang (UC Berkeley); Reuven Gonzales (OasisLabs); Ming-Da Liu Zhang (ETHZ); Nick Pawlowski (Imperial College London); Clement Fung (University of British Columbia)
Abstract
Designing effective REDD+ policies, assessing their GHG impact, and linking them with the corresponding payments, is a resource intensive and complex task. GainForest leverages video prediction with remote sensing to monitor and forecast forest change at high resolution. Furthermore, by viewing payment allocation as a feature selection problem, GainForest can efficiently design payment schemes based on the Shapley value.