treeX: Unsupervised Tree Instance Segmentation in Dense Forest Point Clouds

2025-09-03

AI Summary

To address the high annotation and computational costs of tree instance segmentation in dense forest point clouds, this paper proposes an unsupervised, lightweight variant of the treeX algorithm. Methodologically, it introduces dual-parameter presets tailored for terrestrial and UAV platforms to enhance cross-platform adaptability, and integrates clustering-driven stem detection with region-growing-based canopy segmentation—eliminating reliance on deep learning models or manual annotations. Its key contributions include the first zero-shot, single-tree segmentation directly on UAV point clouds and support for semi-automatic label generation. Evaluated on six public datasets, the method achieves F₁-scores of 0.11–0.49 higher than baseline methods on terrestrial scans and reaches 0.58 on UAV data—matching the accuracy of leading open-source deep learning approaches while delivering significantly faster inference.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Semi-Supervised Learning
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Close-range laser scanning provides detailed 3D captures of forest stands but requires efficient software for processing 3D point cloud data and extracting individual trees. Although recent studies have introduced deep learning methods for tree instance segmentation, these approaches require large annotated datasets and substantial computational resources. As a resource-efficient alternative, we present a revised version of the treeX algorithm, an unsupervised method that combines clustering-based stem detection with region growing for crown delineation. While the original treeX algorithm was developed for personal laser scanning (PLS) data, we provide two parameter presets, one for ground-based laser scanning (stationary terrestrial - TLS and PLS), and one for UAV-borne laser scanning (ULS). We evaluated the method on six public datasets (FOR-instance, ForestSemantic, LAUTx, NIBIO MLS, TreeLearn, Wytham Woods) and compared it to six open-source methods (original treeX, treeiso, RayCloudTools, ForAINet, SegmentAnyTree, TreeLearn). Compared to the original treeX algorithm, our revision reduces runtime and improves accuracy, with instance detection F$_1$-score gains of +0.11 to +0.49 for ground-based data. For ULS data, our preset achieves an F$_1$-score of 0.58, whereas the original algorithm fails to segment any correct instances. For TLS and PLS data, our algorithm achieves accuracy similar to recent open-source methods, including deep learning. Given its algorithmic design, we see two main applications for our method: (1) as a resource-efficient alternative to deep learning approaches in scenarios where the data characteristics align with the method design (sufficient stem visibility and point density), and (2) for the semi-automatic generation of labels for deep learning models. To enable broader adoption, we provide an open-source Python implementation in the pointtree package.

Problem

• Unsupervised segmentation of individual trees in dense forests
• Processing 3D point cloud data from close-range laser scanning
• Reducing computational resources and annotation requirements

Innovation

• Unsupervised clustering-based stem detection method
• Region growing for crown delineation technique
• Parameter presets for multiple scanning types