119 citations · 243 across the 9 of their papers we have counts for
10 papers · 1 filter
Enhancing Tree Species Classification: Insights from YOLOv8 and Explainable AI Applied to TLS Point Cloud Projections
Adrian Straker, Paul Magdon, Marco Zullich +5
Aiming to advance research in the field of interpretability of deep learning models for tree species classification using TLS 3D point clouds we present insights in the classificat…
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.…
Multispectral airborne laser scanning for tree species classification: a benchmark of machine learning and deep learning algorithms
Josef Taher, Eric Hyyppä, Matti Hyyppä +46
Climate-smart and biodiversity-preserving forestry demands precise information on forest resources, extending to the individual tree level. Multispectral airborne laser scanning (A…
BranchPoseNet: Characterizing tree branching with a deep learning-based pose estimation approach
Stefano Puliti, Carolin Fischer, Rasmus Astrup
This paper presents an automated pipeline for detecting tree whorls in proximally laser scanning data using a pose-estimation deep learning model. Accurate whorl detection provides…
Benchmarking tree species classification from proximally-sensed laser scanning data: introducing the FOR-species20K dataset
Stefano Puliti, Emily R. Lines, Jana Müllerová +30
Proximally-sensed laser scanning offers significant potential for automated forest data capture, but challenges remain in automatically identifying tree species without additional…
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…
FOR-instance: a UAV laser scanning benchmark dataset for semantic and instance segmentation of individual trees
Stefano Puliti, Grant Pearse, Peter Surový +4
The FOR-instance dataset (available at https://doi.org/10.5281/zenodo.8287792) addresses the challenge of accurate individual tree segmentation from laser scanning data, crucial fo…
Towards accurate instance segmentation in large-scale LiDAR point clouds
Binbin Xiang, Torben Peters, Theodora Kontogianni +4
Panoptic segmentation is the combination of semantic and instance segmentation: assign the points in a 3D point cloud to semantic categories and partition them into distinct object…
Point2Tree(P2T) -- framework for parameter tuning of semantic and instance segmentation used with mobile laser scanning data in coniferous forest
Maciej Wielgosz, Stefano Puliti, Phil Wilkes +1
This article introduces Point2Tree, a novel framework that incorporates a three-stage process involving semantic segmentation, instance segmentation, optimization analysis of hyper…