Estimating Canopy Height at Scale

Pauls, Jan; Zimmer, Max; Kelly, Una M.; Schwartz, Martin; Saatchi, Sassan; Ciais, Philippe; Pokutta, Sebastian; Brandt, Martin; Gieseke, Fabian


We propose a framework for global-scale canopy height estimation based on satellite data. Our model leverages advanced data preprocessing techniques, resorts to a novel loss function designed to counter geolocation inaccuracies inherent in the ground-truth height measurements, and employs data from the Shuttle Radar Topography Mission to effectively filter out erroneous labels in mountainous regions, enhancing the reliability of our predictions in those areas. A comparison between predictions and ground-truth labels yields an MAE/RMSE of 2.43 / 4.73 (meters) overall and 4.45 / 6.72 (meters) for trees taller than five meters, which depicts a substantial improvement compared to existing global-scale products. The resulting height map as well as the underlying framework will facilitate and enhance ecological analyses at a global scale, including, but not limited to, large-scale forest and biomass monitoring.

canopy height; satellite data; machine learning; biomass

Publication type
Research article in proceedings (conference)

Peer reviewed

Publication status
accepted / in press (not yet published)


International Conference on Machine Learning (ICML)


Book title
Proceedings of the 41st International Conference on Machine Learning