Capturing Temporal Dynamics in Large-Scale Canopy Tree Height Estimation
Abstract
With the rise in global greenhouse gas emissions, accurate large-scale tree canopy height maps are essential for understanding forest structure, estimating above-ground biomass, and monitoring ecological disruptions. To this end, we present a novel approach to generate large-scale, high-resolution canopy height maps over time. Our model accurately predicts canopy height over multiple years given Sentinel 2 time series satellite data. Using GEDI LiDAR data as the ground truth for training the model, we present the first 10 m resolution temporal canopy height map of the European continent for the period 2019–2022. As part of this product, we also offer a detailed canopy height map for 2020, providing more precise estimates than previous studies. Our pipeline and the resulting temporal height map are publicly available, enabling comprehensive large-scale monitoring of forests and, hence, facilitating future research and ecological analyses. For an interactive viewer, see https://europetreemap.projects.earthengine.app/view/europeheight.
Cite
Text
Pauls et al. "Capturing Temporal Dynamics in Large-Scale Canopy Tree Height Estimation." Proceedings of the 42nd International Conference on Machine Learning, 2025.Markdown
[Pauls et al. "Capturing Temporal Dynamics in Large-Scale Canopy Tree Height Estimation." Proceedings of the 42nd International Conference on Machine Learning, 2025.](https://mlanthology.org/icml/2025/pauls2025icml-capturing/)BibTeX
@inproceedings{pauls2025icml-capturing,
title = {{Capturing Temporal Dynamics in Large-Scale Canopy Tree Height Estimation}},
author = {Pauls, Jan and Zimmer, Max and Turan, Berkant and Saatchi, Sassan and Ciais, Philippe and Pokutta, Sebastian and Gieseke, Fabian},
booktitle = {Proceedings of the 42nd International Conference on Machine Learning},
year = {2025},
pages = {48422-48438},
volume = {267},
url = {https://mlanthology.org/icml/2025/pauls2025icml-capturing/}
}