aiqbench analysis
This research introduces BIRCH-Trees, a new benchmark for estimating individual tree height and species using UAV imagery, and presents DINOvTree, a Vision Foundation Model approach that outperforms existing methods with fewer parameters. By leveraging VFMs for simultaneous prediction of structural traits and classification, the study advances scalable computer vision applications for carbon sink monitoring without relying on traditional allometric equations.
Estimating Individual Tree Height and Species from UAV Imagery
arXiv:2603.23669v3 Announce Type: replace-cross Abstract: Accurate estimation of forest biomass, a major carbon sink, relies heavily on tree-level traits such as height and species. Unoccupied Aerial Vehicles (UAVs) capturing high-resolution imagery from a single RGB camera offer a cost-effective and scalable approach for mapping and measuring individual trees. We introduce BIRCH-Trees, the first benchmark for individual tree height and species estimation from tree-centered UAV images, spanning three datasets: temperate forests, tropical forests, and boreal plantations. We also present DINOvTree, a unified approach using a Vision Foundation Model (VFM) backbone with task-specific heads for simultaneous height and species prediction. Through extensive evaluations on BIRCH-Trees, we compare DINOvTree against commonly used vision methods, including VFMs, as well as biological allometric equations. We find that DINOvTree achieves top overall results with accurate height predictions and competitive classification accuracy while using only 54% to 58% of the parameters of the second-best approach.
Abstract:Accurate estimation of forest biomass, a major carbon sink, relies heavily on tree-level traits such as height and species. Unoccupied Aerial Vehicles (UAVs) capturing high-resolution imagery from a single RGB camera offer a cost-effective and scalable approach for mapping and measuring individual trees. We introduce BIRCH-Trees, the first benchmark for individual tree height and species estimation from tree-centered UAV images, spanning three datasets: temperate forests, tropical forests, and boreal plantations. We also present DINOvTree, a unified approach using a Vision Foundation Model (VFM) backbone with task-specific heads for simultaneous height and species prediction. Through extensive evaluations on BIRCH-Trees, we compare DINOvTree against commonly used vision methods, including VFMs, as well as biological allometric equations. We find that DINOvTree achieves top overall results with accurate height predictions and competitive classification accuracy while using only 54% to 58% of the parameters of the second-best approach.
Accepted to ECCV 2026. Project page: this https URL
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
arXiv:2603.23669 [cs.CV]
(or arXiv:2603.23669v3 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2603.23669 Focus to learn more arXiv-issued DOI via DataCite
Computer Vision - ECCV 2026. Lecture Notes in Computer Science (LNCS, volume 17048), pp. 58-76, Springer, Cham, 2026
https://doi.org/10.1007/978-3-032-37372-4_4 Focus to learn more DOI(s) linking to related resources
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