activity
20232026
most citedSegmentAnyTree: A sensor and platform agnostic deep learning model for tree segmentation using laser scanning data

119 citations · 120 across the 3 of their papers we have counts for

collaborators

5 papers

cs.DL2026

3Dtrees.earth: community research infrastructure for tree and forest analysis using close-range LiDAR data

Janusch Vajna-Jehle, Kilian Gerberding, Daniel Lusk +275

From individual trees to forest stands, three-dimensional structure shapes carbon, water, and energy flows, habitat conditions, and resource availability. Yet tree and forest struc…

cs.CV2026

Toward a foundation model for forest point clouds

Yuanwen Yue, Stefano Puliti, Damien Robert +7

Forest inventories increasingly rely on artificial intelligence (AI) models to derive forest attributes from large-scale 3D point clouds. Current models are typically specialized t…

cs.CV2025★ 1 cited

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds

Binbin Xiang, Maciej Wielgosz, Stefano Puliti +4

The segmentation of forest LiDAR 3D point clouds, including both individual tree and semantic segmentation, is fundamental for advancing forest management and ecological research.…

cs.CV2024★ 119 cited

SegmentAnyTree: A sensor and platform agnostic deep learning model for tree segmentation using laser scanning data

Maciej Wielgosz, Stefano Puliti, Binbin Xiang +2

This research advances individual tree crown (ITC) segmentation in lidar data, using a deep learning model applicable to various laser scanning types: airborne (ULS), terrestrial (…

cs.CV2023

Automated forest inventory: analysis of high-density airborne LiDAR point clouds with 3D deep learning

Binbin Xiang, Maciej Wielgosz, Theodora Kontogianni +4

Detailed forest inventories are critical for sustainable and flexible management of forest resources, to conserve various ecosystem services. Modern airborne laser scanners deliver…