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

34 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
2. Combined impact of semantic segmentation and quantitative structure modelling of Southern pine trees using terrestrial laser scanning J. Xia et al.
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.
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.
5. A Deep Learning Time Series Approach for Leaf and Wood Classification from Terrestrial LiDAR Point Clouds T. Han & G. Sánchez-Azofeifa
6. Dynamic region growing approach for leaf-wood separation of individual trees based on geometric features and growing patterns W. Hao & M. Ran
7. Forest-PointNet: A Deep Learning Model for Vertical Structure Segmentation in Complex Forest Scenes Z. Ma et al.
8. Extraction of Liana Stems Using Geometric Features from Terrestrial Laser Scanning Point Clouds T. Han & G. Sánchez-Azofeifa
9. An Improved Convolution Neural Network-Based Model for Classifying Foliage and Woody Components from Terrestrial Laser Scanning Data B. Wu et al.
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.
11. Separating Leaf and Wood Points in Terrestrial Laser Scanning Data Using Multiple Optimal Scales J. Zhou et al.
12. Wood and leaf separation from terrestrial LiDAR point clouds based on mode points evolution Z. Hui et al.
13. Individual Tree Segmentation from Side-View LiDAR Point Clouds of Street Trees Using Shadow-Cut Z. Hua et al.
14. Above-ground biomass references for urban trees from terrestrial laser scanning data D. Kükenbrink et al.
15. SPTNet: Sparse Convolution and Transformer Network for Woody and Foliage Components Separation From Point Clouds S. Zhang et al.
16. LeWoS: A universal leaf‐wood classification method to facilitate the 3D modelling of large tropical trees using terrestrial LiDAR D. Wang et al.
17. LWSNet: A Point-Based Segmentation Network for Leaf-Wood Separation of Individual Trees T. Jiang et al.
18. Weld Bead Detection Based on 3D Geometric Features and Machine Learning Approaches P. Rodriguez-Gonzalvez & M. Rodriguez-Martin
19. Benchmarking Geometry-Based Leaf-Filtering Algorithms for Tree Volume Estimation Using Terrestrial LiDAR Scanners M. Ali et al.
20. Separation of Wood and Foliage for Trees From Ground Point Clouds Using a Novel Least-Cost Path Model S. Xu et al.
21. Automated Segmentation of Individual Tree Structures Using Deep Learning over LiDAR Point Cloud Data D. Kim et al.
22. A novel and efficient method for wood–leaf separation from terrestrial laser scanning point clouds at the forest plot level P. Wan et al.
23. Improved Supervised Learning-Based Approach for Leaf and Wood Classification From LiDAR Point Clouds of Forests S. Krishna Moorthy et al.
24. Stem Detection from Terrestrial Laser Scanning Data with Features Selected via Stem-Based Evaluation M. Chen et al.
25. Estimating Tree Structural Parameters via Automatic Tree Segmentation From LiDAR Point Cloud Data K. Itakura et al.
26. Potential of Using Terrestrial Laser Scanning in Mining Industry L. Nguyen & M. Dang
27. Terrestrial Laser Scanning for Quantifying Timber Assortments from Standing Trees in a Mixed and Multi-Layered Mediterranean Forest C. Alvites et al.
28. Discrimination of Leaves in a Multi-Layered Mediterranean Forest through Machine Learning Algorithms C. Alvites et al.
29. Detailed Land Cover Mapping from Multitemporal Landsat-8 Data of Different Cloud Cover C. Karakizi et al.
30. Towards Tree Green Crown Volume: A Methodological Approach Using Terrestrial Laser Scanning Z. Zhu et al.
31. Refined Aboveground Biomass Estimation of Moso Bamboo Forest Using Culm Lengths Extracted from TLS Point Cloud R. Jiang et al.
32. Measuring the Contribution of Leaves to the Structural Complexity of Urban Tree Crowns with Terrestrial Laser Scanning G. Arseniou et al.
33. Leaf–Wood Separation in Tree Point Clouds via Unlearning-Driven Supervoxel Random Forest W. Wang et al.
34. Separating Tree Photosynthetic and Non-Photosynthetic Components from Point Cloud Data Using Dynamic Segment Merging D. Wang et al.