119 citations · 148 across the 6 of their papers we have counts for
7 papers
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…
SegmentAnyTreeV2: Scaling Transformer-Based Tree Instance Segmentation Across Sensors, Platforms, and Forests
Maciej Wielgosz, Stefano Puliti, Rasmus Astrup
We present SegmentAnyTreeV2, a sensor- and platform-agnostic framework for semantic and instance segmentation of forest point clouds. The model combines a serialization-based Point…
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.…
Spatiotemporal Analysis of Forest Machine Operations Using 3D Video Classification
Maciej Wielgosz, Simon Berg, Heikki Korpunen +1
This paper presents a deep learning-based framework for classifying forestry operations from dashcam video footage. Focusing on four key work elements - crane-out, cutting-and-to-p…
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…