[ICML24] Global-Scale Forest Height Estimation
Our paper Estimating Canopy Height at Scale was accepted to ICML 2024! In this work, we present a novel framework for global-scale forest height estimation. Using a deep learning approach that leverages large amounts of satellite data with only sparsely distributed ground-truth height measurements from NASA's GEDI mission, we achieve state-of-the-art accuracy with MAE/RMSE of 2.43m/4.73m overall, significantly outperforming existing approaches. The resulting height map facilitates ecological analyses at a global scale.