Estimating canopy height at scale
- 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.
| Author: | Jan Pauls, Max Zimmer, Una M Kelly, Martin Schwartz, Sassan Saatchi, Philippe Ciais, Sebastian Pokutta, Martin Brandt, Fabian Gieseke |
|---|---|
| Document Type: | In Proceedings |
| Parent Title (English): | Proceedings of the 41st International Conference on Machine Learning |
| Volume: | 235 |
| First Page: | 39972 |
| Last Page: | 39988 |
| Series: | PMLR |
| Year of first publication: | 2024 |
| URL: | https://raw.githubusercontent.com/mlresearch/v235/main/assets/pauls24a/pauls24a.pdf |
