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

32 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 10.1080/01431161.2023.2217981
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4. A Deep Learning Time Series Approach for Leaf and Wood Classification from Terrestrial LiDAR Point Clouds T. Han & G. Sánchez-Azofeifa 10.3390/rs14133157
5. Dynamic region growing approach for leaf-wood separation of individual trees based on geometric features and growing patterns W. Hao & M. Ran 10.1080/01431161.2024.2394235
6. Forest-PointNet: A Deep Learning Model for Vertical Structure Segmentation in Complex Forest Scenes Z. Ma et al. 10.3390/rs15194793
7. Extraction of Liana Stems Using Geometric Features from Terrestrial Laser Scanning Point Clouds T. Han & G. Sánchez-Azofeifa 10.3390/rs14164039
8. An Improved Convolution Neural Network-Based Model for Classifying Foliage and Woody Components from Terrestrial Laser Scanning Data B. Wu et al. 10.3390/rs12061010
9. MDC-Net: a multi-directional constrained and prior assisted neural network for wood and leaf separation from terrestrial laser scanning W. Dai et al. 10.1080/17538947.2023.2198261
10. Separating Leaf and Wood Points in Terrestrial Laser Scanning Data Using Multiple Optimal Scales J. Zhou et al. 10.3390/s19081852
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15. LeWoS: A universal leaf‐wood classification method to facilitate the 3D modelling of large tropical trees using terrestrial LiDAR D. Wang et al. 10.1111/2041-210X.13342
16. LWSNet: A Point-Based Segmentation Network for Leaf-Wood Separation of Individual Trees T. Jiang et al. 10.3390/f14071303
17. Weld Bead Detection Based on 3D Geometric Features and Machine Learning Approaches P. Rodriguez-Gonzalvez & M. Rodriguez-Martin 10.1109/ACCESS.2019.2891367
18. Benchmarking Geometry-Based Leaf-Filtering Algorithms for Tree Volume Estimation Using Terrestrial LiDAR Scanners M. Ali et al. 10.3390/rs16061021
19. Separation of Wood and Foliage for Trees From Ground Point Clouds Using a Novel Least-Cost Path Model S. Xu et al. 10.1109/JSTARS.2021.3090502
20. Automated Segmentation of Individual Tree Structures Using Deep Learning over LiDAR Point Cloud Data D. Kim et al. 10.3390/f14061159
21. A novel and efficient method for wood–leaf separation from terrestrial laser scanning point clouds at the forest plot level P. Wan et al. 10.1111/2041-210X.13715
22. Improved Supervised Learning-Based Approach for Leaf and Wood Classification From LiDAR Point Clouds of Forests S. Krishna Moorthy et al. 10.1109/TGRS.2019.2947198
23. Stem Detection from Terrestrial Laser Scanning Data with Features Selected via Stem-Based Evaluation M. Chen et al. 10.3390/f14102035
24. Estimating Tree Structural Parameters via Automatic Tree Segmentation From LiDAR Point Cloud Data K. Itakura et al. 10.1109/JSTARS.2021.3135491
25. Terrestrial Laser Scanning for Quantifying Timber Assortments from Standing Trees in a Mixed and Multi-Layered Mediterranean Forest C. Alvites et al. 10.3390/rs13214265
26. Discrimination of Leaves in a Multi-Layered Mediterranean Forest through Machine Learning Algorithms C. Alvites et al. 10.3390/rs15184450
27. Detailed Land Cover Mapping from Multitemporal Landsat-8 Data of Different Cloud Cover C. Karakizi et al. 10.3390/rs10081214
28. Towards Tree Green Crown Volume: A Methodological Approach Using Terrestrial Laser Scanning Z. Zhu et al. 10.3390/rs12111841
29. Refined Aboveground Biomass Estimation of Moso Bamboo Forest Using Culm Lengths Extracted from TLS Point Cloud R. Jiang et al. 10.3390/rs14215537
30. Measuring the Contribution of Leaves to the Structural Complexity of Urban Tree Crowns with Terrestrial Laser Scanning G. Arseniou et al. 10.3390/rs13142773
31. Leaf–Wood Separation in Tree Point Clouds via Unlearning-Driven Supervoxel Random Forest W. Wang et al. 10.1109/TGRS.2025.3601652
32. Separating Tree Photosynthetic and Non-Photosynthetic Components from Point Cloud Data Using Dynamic Segment Merging D. Wang et al. 10.3390/f9050252