CLASSIFICATION OF TREE SPECIES AND STANDING DEAD TREES BY FUSING UAV-BASED LIDAR DATA AND MULTISPECTRAL IMAGERY IN THE 3D DEEP NEURAL NETWORK POINTNET++

36 citations as recorded by crossref.

1. Automated measurement of field crop phenotypic traits using UAV 3D point clouds and an improved PointNet++ J. Yao et al. 10.3389/fpls.2025.1654232
2. Classification of single tree decay stages from combined airborne LiDAR data and CIR imagery T. Wong et al. 10.1080/10095020.2024.2311861
3. Machine Learning for Precision Agriculture Using Imagery from Unmanned Aerial Vehicles (UAVs): A Survey I. Zualkernan et al. 10.3390/drones7060382
4. Silvi-Net – A dual-CNN approach for combined classification of tree species and standing dead trees from remote sensing data S. Briechle et al. 10.1016/j.jag.2020.102292
5. LiDAR and multispectral imagery predict the occurrence of tree cavities suitable for a cavity‐nesting duck E. Zlonis et al. 10.1002/rse2.236
6. Urban Tree Species Classification Using UAV-Based Multispectral Images and LiDAR Point Clouds X. Li et al. 10.1007/s41651-023-00167-9
7. A Multifeature Fusion Network for Tree Species Classification Based on Ground-Based LiDAR Data Y. Liu et al. 10.1109/JSTARS.2025.3527808
8. Detection of European Aspen (Populus tremula L.) Based on an Unmanned Aerial Vehicle Approach in Boreal Forests A. Kuzmin et al. 10.3390/rs13091723
9. Local region-learning modules for point cloud classification K. Turgut & H. Dutagaci 10.1007/s00138-023-01495-y
10. Latest Trends on Tree Classification and Segmentation Using UAV Data—A Review of Agroforestry Applications B. Chehreh et al. 10.3390/rs15092263
11. Efficient tree species classification using machine and deep learning algorithms based on UAV-LiDAR data in North China H. Zhang et al. 10.3389/ffgc.2025.1431603
12. Review on Convolutional Neural Networks (CNN) in vegetation remote sensing T. Kattenborn et al. 10.1016/j.isprsjprs.2020.12.010
13. PointNet++ (MSG-DSLS): A Classification Framework With Dynamic Step-Size Loop-Sampling for High-Density Tree Point Clouds Z. Liu et al. 10.1109/JSTARS.2025.3557445
14. Identifying conifer mortality induced by Armillaria root disease using airborne lidar and orthoimagery in south central Oregon B. Oblinger et al. 10.1016/j.foreco.2022.120126
15. A multi-scale approach to detecting standing dead trees in UAV RGB images based on improved faster R-CNN X. Jiang et al. 10.1371/journal.pone.0281084
16. Integrating machine learning, remote sensing and citizen science to create an early warning system for biodiversity A. Antonelli et al. 10.1002/ppp3.10337
17. Health Assessment of Eucalyptus Trees Using Siamese Network from Google Street and Ground Truth Images A. Khan et al. 10.3390/rs13112194
18. Automated Segmentation of Individual Tree Structures Using Deep Learning over LiDAR Point Cloud Data D. Kim et al. 10.3390/f14061159
19. Classification of Tree Species and Standing Dead Trees with Lidar Point Clouds Using Two Deep Neural Networks: PointCNN and 3DmFV-Net M. Hell et al. 10.1007/s41064-022-00200-4
20. Comparing field measurements and annotations as training data for UAV-based detection of standing dead trees in coniferous forest E. Heinaro et al. 10.1080/01431161.2023.2248561
21. Enhancing forest inventory Accuracy: Comparing 3D-CNN and k-NN with genetic algorithm Approaches using ALS data across boreal bioregions A. Balazs et al. 10.1016/j.compag.2025.110576
22. Low Cost Automatic Reconstruction of Tree Structure by AdQSM with Terrestrial Close-Range Photogrammetry Y. Dong et al. 10.3390/f12081020
23. Improving urban tree species classification by deep-learning based fusion of digital aerial images and LiDAR M. Ferreira et al. 10.1016/j.ufug.2024.128240
24. Evaluation of UAS LiDAR data for tree segmentation and diameter estimation in boreal forests using trunk- and crown-based methods M. Kukkonen et al. 10.1139/cjfr-2021-0217
25. UAV-Based Detection of Deciduous Tree Species Using Structural and Spectral Characteristics M. Naseri & S. Shataee Jouibary 10.1007/s12524-024-01944-9
26. Multi-information PointNet++ fusion method for DEM construction from airborne LiDAR data H. Hu et al. 10.1080/10106049.2022.2153929
27. Deeply supervised network for airborne LiDAR tree classification incorporating dual attention mechanisms Z. Zhang et al. 10.1080/15481603.2024.2303866
28. Multispectral LiDAR Data Classification Method Based on an Improved PointNet++ Model 庄. 景 10.12677/GST.2024.121009
29. Forest-PointNet: A Deep Learning Model for Vertical Structure Segmentation in Complex Forest Scenes Z. Ma et al. 10.3390/rs15194793
30. Tree Species Classification of Backpack Laser Scanning Data Using the PointNet++ Point Cloud Deep Learning Method B. Liu et al. 10.3390/rs14153809
31. Tree Species Classification Using Ground-Based LiDAR Data by Various Point Cloud Deep Learning Methods B. Liu et al. 10.3390/rs14225733
32. Remote Sensing Object Detection in the Deep Learning Era—A Review S. Gui et al. 10.3390/rs16020327
33. Tree species classification in an extensive forest area using airborne hyperspectral data under varying light conditions W. Jia & Y. Pang 10.1007/s11676-022-01593-z
34. Analysis of forest tree dieback using UltraCam and UAV imagery M. Naseri et al. 10.1080/02827581.2023.2231349
35. An Attention-Based 3D Convolutional Autoencoder for Few-Shot Hyperspectral Unmixing and Classification C. Li et al. 10.3390/rs15020451
36. MMTSCNet: Multimodal Tree Species Classification Network for Classification of Multi-Source, Single-Tree LiDAR Point Clouds J. Vahrenhold et al. 10.3390/rs17071304