  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 maps. 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.
