Estimating Canopy Height at Scale

Jan Pauls, Max Zimmer, Una M. Kelly, Martin Schwartz, Sassan Saatchi, Philippe Ciais, Sebastian Pokutta, Martin Brandt, Fabian Gieseke
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:39972-39988, 2024.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v235-pauls24a, title = {Estimating Canopy Height at Scale}, author = {Pauls, Jan and Zimmer, Max and Kelly, Una M. and Schwartz, Martin and Saatchi, Sassan and Ciais, Philippe and Pokutta, Sebastian and Brandt, Martin and Gieseke, Fabian}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, pages = {39972--39988}, year = {2024}, editor = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix}, volume = {235}, series = {Proceedings of Machine Learning Research}, month = {21--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v235/main/assets/pauls24a/pauls24a.pdf}, url = {https://proceedings.mlr.press/v235/pauls24a.html}, abstract = {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.} }
Endnote
%0 Conference Paper %T Estimating Canopy Height at Scale %A Jan Pauls %A Max Zimmer %A Una M. Kelly %A Martin Schwartz %A Sassan Saatchi %A Philippe Ciais %A Sebastian Pokutta %A Martin Brandt %A Fabian Gieseke %B Proceedings of the 41st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2024 %E Ruslan Salakhutdinov %E Zico Kolter %E Katherine Heller %E Adrian Weller %E Nuria Oliver %E Jonathan Scarlett %E Felix Berkenkamp %F pmlr-v235-pauls24a %I PMLR %P 39972--39988 %U https://proceedings.mlr.press/v235/pauls24a.html %V 235 %X 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.
APA
Pauls, J., Zimmer, M., Kelly, U.M., Schwartz, M., Saatchi, S., Ciais, P., Pokutta, S., Brandt, M. & Gieseke, F.. (2024). Estimating Canopy Height at Scale. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research 235:39972-39988 Available from https://proceedings.mlr.press/v235/pauls24a.html.

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