Bibliographic Details
| Title: |
Estimating Canopy Height at Scale |
| Authors: |
Pauls, Jan, Zimmer, Max, Kelly, Una M., Schwartz, Martin, Saatchi, Sassan, Ciais, Philippe, Pokutta, Sebastian, Brandt, Martin, Gieseke, Fabian |
| Publication Year: |
2024 |
| Collection: |
Computer Science |
| Subject Terms: |
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning |
| More Details: |
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.
Comment: ICML Camera-Ready, 17 pages, 14 figures, 7 tables |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2406.01076 |
| Accession Number: |
edsarx.2406.01076 |
| Database: |
arXiv |
