Capturing Temporal Dynamics in Tree Canopy Height
Our paper Capturing Temporal Dynamics in Large-Scale Canopy Tree Height Estimation was accepted to ICML 2025! In this work, we present a novel approach to generate large-scale, high-resolution canopy height maps over time. Using Sentinel-2 time series satellite data and GEDI LiDAR data as ground truth, we present the first 10m resolution temporal canopy height map of the European continent for the period 2019-2022. Our pipeline and the resulting temporal height map are publicly available, enabling comprehensive large-scale monitoring of forests.
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.