1 citations · 1 across the 1 of their papers we have counts for
3 papers
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.…
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 (…
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…