arXiv:2603. 23669v2 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.
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The paper introduces BalSAM, a model that combines the Segment Anything Model (SAM) with Digital Surface Model (DSM) elevation data to improve tree crown instance segmentation from high‑resolution drone imagery. Experiments across boreal plantations, temperate forests, and tropical forests show that while off‑the‑shelf SAM does not beat a custom Mask R-CNN, fine‑tuning SAM end‑to‑end and incorporating DSM information yield promising results, especially for plantation sites.
arXiv:2606. 20291v1 Announce Type: new Abstract: Remote sensing is increasingly relied upon to deliver actionable science for forest and wildfire risk management across large landscapes.
arXiv:2606.20223v2 Announce Type: replace Abstract: Camera-trap monitoring in African tropical forests increasingly extends beyond closed-canopy interiors to riverbanks, clearings, and park edges. Am...
arXiv:2510. 09458v2 Announce Type: replace-cross Abstract: Interest in forestry automation is growing alongside rapid advances in deep learning.
arXiv:2608.30149v1 Announce Type: new Abstract: Individual tree measurements derived from Light Detection and Ranging (LiDAR) mounted on Unmanned Aerial Vehicles (UAV) provide valuable information fo...
arXiv:2609.26549v1 Announce Type: new Abstract: Individual tree crown segmentation from aerial imagery underpins tree-level carbon accounting, biodiversity, and restoration monitoring at landscape sc...