FEASIBILITY OF MACHINE LEARNING METHODS FOR SEPARATING WOOD AND LEAF POINTS FROM TERRESTRIAL LASER SCANNING DATA

36 citations as recorded by crossref.

1. A novel geometric feature-based wood-leaf separation method for large and crown-heavy tropical trees using handheld laser scanning point cloud M. Wang & M. Wong https://doi.org/10.1080/01431161.2023.2217981
2. Combined impact of semantic segmentation and quantitative structure modelling of Southern pine trees using terrestrial laser scanning J. Xia et al. https://doi.org/10.1038/s41598-025-09681-w
3. Terrestrial Laser Scanning (TLS) for tree structure studies: a review of methods for wood-leaf classifications from 3D point clouds S. Arrizza et al. https://doi.org/10.1016/j.rsase.2024.101364
4. Advancing terrestrial laser scanning for 3D classification of Spanish moss (Tillandsia usneoides L.) using unsupervised, machine learning, and deep learning methods A. Gaskins et al. https://doi.org/10.1038/s41598-026-49230-7
5. A Deep Learning Time Series Approach for Leaf and Wood Classification from Terrestrial LiDAR Point Clouds T. Han & G. Sánchez-Azofeifa https://doi.org/10.3390/rs14133157
6. Dynamic region growing approach for leaf-wood separation of individual trees based on geometric features and growing patterns W. Hao & M. Ran https://doi.org/10.1080/01431161.2024.2394235
7. Forest-PointNet: A Deep Learning Model for Vertical Structure Segmentation in Complex Forest Scenes Z. Ma et al. https://doi.org/10.3390/rs15194793
8. Extraction of Liana Stems Using Geometric Features from Terrestrial Laser Scanning Point Clouds T. Han & G. Sánchez-Azofeifa https://doi.org/10.3390/rs14164039
9. An Improved Convolution Neural Network-Based Model for Classifying Foliage and Woody Components from Terrestrial Laser Scanning Data B. Wu et al. https://doi.org/10.3390/rs12061010
10. MDC-Net: a multi-directional constrained and prior assisted neural network for wood and leaf separation from terrestrial laser scanning W. Dai et al. https://doi.org/10.1080/17538947.2023.2198261
11. Separating Leaf and Wood Points in Terrestrial Laser Scanning Data Using Multiple Optimal Scales J. Zhou et al. https://doi.org/10.3390/s19081852
12. Wood and leaf separation from terrestrial LiDAR point clouds based on mode points evolution Z. Hui et al. https://doi.org/10.1016/j.isprsjprs.2021.06.012
13. Individual Tree Segmentation from Side-View LiDAR Point Clouds of Street Trees Using Shadow-Cut Z. Hua et al. https://doi.org/10.3390/rs14225742
14. Above-ground biomass references for urban trees from terrestrial laser scanning data D. Kükenbrink et al. https://doi.org/10.1093/aob/mcab002
15. SPTNet: Sparse Convolution and Transformer Network for Woody and Foliage Components Separation From Point Clouds S. Zhang et al. https://doi.org/10.1109/TGRS.2024.3376454
16. LeWoS: A universal leaf‐wood classification method to facilitate the 3D modelling of large tropical trees using terrestrial LiDAR D. Wang et al. https://doi.org/10.1111/2041-210X.13342
17. LWSNet: A Point-Based Segmentation Network for Leaf-Wood Separation of Individual Trees T. Jiang et al. https://doi.org/10.3390/f14071303
18. Automated Wood and Leaf Separation: A Comprehensive Review on Advanced Methods and Forestry Applications T. Vu et al. https://doi.org/10.1109/ACCESS.2026.3709955
19. Stem Modeling for Savanna Tree Characterization with Close-Range Photogrammetry: Comparison of the Performance of Automatic Algorithms F. Laly et al. https://doi.org/10.1080/10549811.2026.2666527
20. Weld Bead Detection Based on 3D Geometric Features and Machine Learning Approaches P. Rodriguez-Gonzalvez & M. Rodriguez-Martin https://doi.org/10.1109/ACCESS.2019.2891367
21. Benchmarking Geometry-Based Leaf-Filtering Algorithms for Tree Volume Estimation Using Terrestrial LiDAR Scanners M. Ali et al. https://doi.org/10.3390/rs16061021
22. Separation of Wood and Foliage for Trees From Ground Point Clouds Using a Novel Least-Cost Path Model S. Xu et al. https://doi.org/10.1109/JSTARS.2021.3090502
23. Automated Segmentation of Individual Tree Structures Using Deep Learning over LiDAR Point Cloud Data D. Kim et al. https://doi.org/10.3390/f14061159
24. A novel and efficient method for wood–leaf separation from terrestrial laser scanning point clouds at the forest plot level P. Wan et al. https://doi.org/10.1111/2041-210X.13715
25. Improved Supervised Learning-Based Approach for Leaf and Wood Classification From LiDAR Point Clouds of Forests S. Krishna Moorthy et al. https://doi.org/10.1109/TGRS.2019.2947198
26. Stem Detection from Terrestrial Laser Scanning Data with Features Selected via Stem-Based Evaluation M. Chen et al. https://doi.org/10.3390/f14102035
27. Estimating Tree Structural Parameters via Automatic Tree Segmentation From LiDAR Point Cloud Data K. Itakura et al. https://doi.org/10.1109/JSTARS.2021.3135491
28. Potential of Using Terrestrial Laser Scanning in Mining Industry L. Nguyen & M. Dang https://doi.org/10.15407/ugz2025.04.051
29. Terrestrial Laser Scanning for Quantifying Timber Assortments from Standing Trees in a Mixed and Multi-Layered Mediterranean Forest C. Alvites et al. https://doi.org/10.3390/rs13214265
30. Discrimination of Leaves in a Multi-Layered Mediterranean Forest through Machine Learning Algorithms C. Alvites et al. https://doi.org/10.3390/rs15184450
31. Detailed Land Cover Mapping from Multitemporal Landsat-8 Data of Different Cloud Cover C. Karakizi et al. https://doi.org/10.3390/rs10081214
32. Towards Tree Green Crown Volume: A Methodological Approach Using Terrestrial Laser Scanning Z. Zhu et al. https://doi.org/10.3390/rs12111841
33. Refined Aboveground Biomass Estimation of Moso Bamboo Forest Using Culm Lengths Extracted from TLS Point Cloud R. Jiang et al. https://doi.org/10.3390/rs14215537
34. Measuring the Contribution of Leaves to the Structural Complexity of Urban Tree Crowns with Terrestrial Laser Scanning G. Arseniou et al. https://doi.org/10.3390/rs13142773
35. Leaf–Wood Separation in Tree Point Clouds via Unlearning-Driven Supervoxel Random Forest W. Wang et al. https://doi.org/10.1109/TGRS.2025.3601652
36. Separating Tree Photosynthetic and Non-Photosynthetic Components from Point Cloud Data Using Dynamic Segment Merging D. Wang et al. https://doi.org/10.3390/f9050252