CONDITIONAL RANDOM FIELDS FOR LIDAR POINT CLOUD CLASSIFICATION IN COMPLEX URBAN AREAS

63 citations as recorded by crossref.

1. A Classification-Segmentation Framework for the Detection of Individual Trees in Dense MMS Point Cloud Data Acquired in Urban Areas M. Weinmann et al. 10.3390/rs9030277
2. Structure-Aware Convolution for 3D Point Cloud Classification and Segmentation L. Wang et al. 10.3390/rs12040634
3. Performance of unsupervised machine learning methods using chi-squared weights for LiDAR point cloud filtering in urban areas A. Sen et al. 10.1080/14498596.2021.2013329
4. Directionally constrained fully convolutional neural network for airborne LiDAR point cloud classification C. Wen et al. 10.1016/j.isprsjprs.2020.02.004
5. Lightweight Semantic Architecture Modeling by 3D Feature Line Detection S. Xu et al. 10.3390/rs15081957
6. Target-aware attentional network for rare class segmentation in large-scale LiDAR point clouds X. Zhang et al. 10.1016/j.isprsjprs.2024.11.012
7. Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers M. Weinmann et al. 10.1016/j.isprsjprs.2015.01.016
8. Airborne lidar data classification in complex urban area using random forest: a case study of Bergama, Turkey S. CANAZ SEVGEN 10.26833/ijeg.440828
9. Classification Model of Point Cloud Along Transmission Line Based on Group Normalization Z. Yin et al. 10.3389/fenrg.2022.839273
10. Airborne LiDAR point cloud classification with global-local graph attention convolution neural network C. Wen et al. 10.1016/j.isprsjprs.2021.01.007
11. A Bayesian-Network-Based Classification Method Integrating Airborne LiDAR Data With Optical Images Z. Kang et al. 10.1109/JSTARS.2016.2628775
12. Parameter optimization criteria guided 3D point cloud classification H. Li et al. 10.1007/s11042-018-6838-z
13. Adaptive neighborhood size and effective geometric features selection for 3D scattered point cloud classification M. Günen 10.1016/j.asoc.2021.108196
14. Robust Building Detection in Urban Environments from Airborne LiDAR Data: A Geometry-Based Approach R. dos Santos & M. Galo 10.1109/JSTARS.2024.3391050
15. Deep convolutional neural networks for building extraction from orthoimages and dense image matching point clouds E. Maltezos et al. 10.1117/1.JRS.11.042620
16. Urban land cover classification using airborne LiDAR data: A review W. Yan et al. 10.1016/j.rse.2014.11.001
17. Using multi-scale and hierarchical deep convolutional features for 3D semantic classification of TLS point clouds Z. Guo & C. Feng 10.1080/13658816.2018.1552790
18. Two-Layered Graph-Cuts-Based Classification of LiDAR Data in Urban Areas Y. Yang et al. 10.3390/s19214685
19. Super-Resolution-Based Snake Model—An Unsupervised Method for Large-Scale Building Extraction Using Airborne LiDAR Data and Optical Image T. Nguyen et al. 10.3390/rs12111702
20. A top-down strategy for buildings extraction from complex urban scenes using airborne LiDAR point clouds R. Huang et al. 10.1016/j.infrared.2018.05.021
21. Airborne LiDAR point cloud classification using PointNet++ network with full neighborhood features X. Nong et al. 10.1371/journal.pone.0280346
22. A Review of panoptic segmentation for mobile mapping point clouds B. Xiang et al. 10.1016/j.isprsjprs.2023.08.008
23. A Multi-Primitive-Based Hierarchical Optimal Approach for Semantic Labeling of ALS Point Clouds X. Ge et al. 10.3390/rs11101243
24. A Hierarchical Approach for Point Cloud Classification With 3D Contextual Features C. Feng & Z. Guo 10.1109/JSTARS.2021.3077568
25. Fusion of airborne laserscanning point clouds and images for supervised and unsupervised scene classification M. Gerke & J. Xiao 10.1016/j.isprsjprs.2013.10.011
26. Classification of Stereo Images from Mobile Mapping Data Using Conditional Random Fields M. Coenen et al. 10.1007/s41064-017-0004-5
27. 3D reconstruction of buildings from LiDAR data considering various types of roof structures P. Pahlavani et al. 10.1080/01431161.2017.1280624
28. Linking Points With Labels in 3D: A Review of Point Cloud Semantic Segmentation Y. Xie et al. 10.1109/MGRS.2019.2937630
29. Semiautomatic Workflow for Accurate LiDAR-Derived DSM Retrieval in Aquatic Scenarios via Water Surface Mapping E. Piaser et al. 10.1109/JSTARS.2025.3602156
30. Efficient terrestrial laser scan segmentation exploiting data structure H. Mahmoudabadi et al. 10.1016/j.isprsjprs.2016.05.015
31. Construction of a Semantic Segmentation Network for the Overhead Catenary System Point Cloud Based on Multi-Scale Feature Fusion T. Xu et al. 10.3390/rs14122768
32. Comparison of heuristic and deep learning-based methods for ground classification from aerial point clouds M. Soilán et al. 10.1080/17538947.2019.1663948
33. Automating Parameter Learning for Classifying Terrestrial LiDAR Point Cloud Using 2D Land Cover Maps C. Feng & Z. Guo 10.3390/rs10081192
34. A Multiscale and Hierarchical Feature Extraction Method for Terrestrial Laser Scanning Point Cloud Classification Z. Wang et al. 10.1109/TGRS.2014.2359951
35. A Semantic Segmentation Network for ALS Point Clouds Considering Geographic Location and Feature Interaction Z. Wang et al. 10.1109/TGRS.2025.3572414
36. Graph Attention Feature Fusion Network for ALS Point Cloud Classification J. Yang et al. 10.3390/s21186193
37. Contour Extraction in Buildings in Airborne LiDAR Point Clouds Using Multiscale Local Geometric Descriptors and Visual Analytics J. Sreevalsan-Nair et al. 10.1109/JSTARS.2018.2833801
38. Exploring Semantic Information Extraction From Different Data Forms in 3D Point Cloud Semantic Segmentation A. Zhang et al. 10.1109/ACCESS.2023.3287940
39. Improving LiDAR classification accuracy by contextual label smoothing in post-processing N. Li et al. 10.1016/j.isprsjprs.2018.11.022
40. A Review of Deep Learning-Based Semantic Segmentation for Point Cloud J. Zhang et al. 10.1109/ACCESS.2019.2958671
41. Multilevel intuitive attention neural network for airborne LiDAR point cloud semantic segmentation Z. Wang et al. 10.1016/j.jag.2024.104020
42. Ground and Multi-Class Classification of Airborne Laser Scanner Point Clouds Using Fully Convolutional Networks A. Rizaldy et al. 10.3390/rs10111723
43. Aerial Point Cloud Classification Using an Alternative Approach for the Dynamic Computation of K-Nearest Neighbors I. Pârvu et al. 10.2478/jaes-2020-0023
44. Building Extraction from Airborne LiDAR Data Based on Multi-Constraints Graph Segmentation Z. Hui et al. 10.3390/rs13183766
45. A Discriminative Tensor Representation Model for Feature Extraction and Classification of Multispectral LiDAR Data Q. Wang & Y. Gu 10.1109/TGRS.2019.2947081
46. BIM-driven data augmentation method for semantic segmentation in superpoint-based deep learning network R. Zhai et al. 10.1016/j.autcon.2022.104373
47. OPALS – A framework for Airborne Laser Scanning data analysis N. Pfeifer et al. 10.1016/j.compenvurbsys.2013.11.002
48. Locating and Dating Sites Using Lidar Survey in a Mosaic Landscape in Western Belize J. Yaeger et al. 10.7183/2326-3768.4.3.339
49. Structure-based object detection from scene point clouds W. Hao & Y. Wang 10.1016/j.neucom.2015.12.101
50. Distinctive 2D and 3D features for automated large-scale scene analysis in urban areas M. Weinmann et al. 10.1016/j.cag.2015.01.006
51. Point Cloud vs. Mesh Features for Building Interior Classification M. Bassier et al. 10.3390/rs12142224
52. Multigranularity Semantic Labeling of Point Clouds for the Measurement of the Rail Tanker Component With Structure Modeling C. Qi & J. Yin 10.1109/TIM.2020.3027406
53. Analytical and numerical investigations on the accuracy and robustness of geometric features extracted from 3D point cloud data A. Dittrich et al. 10.1016/j.isprsjprs.2017.02.012
54. Robust and fast reconstruction of complex roofs with active sampling from 3D point clouds Y. Dehbi et al. 10.1111/tgis.12659
55. Contextual classification of lidar data and building object detection in urban areas J. Niemeyer et al. 10.1016/j.isprsjprs.2013.11.001
56. MSNet: Multi-Scale Convolutional Network for Point Cloud Classification L. Wang et al. 10.3390/rs10040612
57. A Hybrid Semantic Point Cloud Classification-Segmentation Framework Based on Geometric Features and Semantic Rules M. Weinmann et al. 10.1007/s41064-017-0020-5
58. Moving objects extraction for building environmental maps describing human walking activity using a 3D LiDAR T. Nishio & M. Niitsuma 10.1080/01691864.2022.2126732
59. Coarse-to-Fine Registration of Airborne LiDAR Data and Optical Imagery on Urban Scenes T. Nguyen et al. 10.1109/JSTARS.2020.2987305
60. Improving trajectory estimation using 3D city models and kinematic point clouds L. Lucks et al. 10.1111/tgis.12719
61. A Comparison of Deep Learning Methods for Airborne Lidar Point Clouds Classification N. Li et al. 10.1109/JSTARS.2021.3091389
62. DANCE-NET: Density-aware convolution networks with context encoding for airborne LiDAR point cloud classification X. Li et al. 10.1016/j.isprsjprs.2020.05.023
63. 3D micro-mapping: Towards assessing the quality of crowdsourcing to support 3D point cloud analysis B. Herfort et al. 10.1016/j.isprsjprs.2018.01.009