CONTEXTUAL CLASSIFICATION OF POINT CLOUD DATA BY EXPLOITING INDIVIDUAL 3D NEIGBOURHOODS

54 citations as recorded by crossref.

1. Feature-preserving 3D mesh simplification for urban buildings M. Li & L. Nan 10.1016/j.isprsjprs.2021.01.006
2. Deep Learning Model for Wind Forecasting: Classification Analyses for Temporal Meteorological Data S. Harbola & V. Coors 10.1007/s41064-021-00185-6
3. A Simple Semantic-Based Data Storage Layout for Querying Point Clouds S. El-Mahgary et al. 10.3390/ijgi9020072
4. PointNet++ Network Architecture with Individual Point Level and Global Features on Centroid for ALS Point Cloud Classification Y. Chen et al. 10.3390/rs13030472
5. A structured regularization framework for spatially smoothing semantic labelings of 3D point clouds L. Landrieu et al. 10.1016/j.isprsjprs.2017.08.010
6. Contextual segment-based classification of airborne laser scanner data G. Vosselman et al. 10.1016/j.isprsjprs.2017.03.010
7. TUM-MLS-2016: An Annotated Mobile LiDAR Dataset of the TUM City Campus for Semantic Point Cloud Interpretation in Urban Areas J. Zhu et al. 10.3390/rs12111875
8. Creating 3D city models with building footprints and LIDAR point cloud classification: A machine learning approach Y. Park & J. Guldmann 10.1016/j.compenvurbsys.2019.01.004
9. Modelling of buildings from aerial LiDAR point clouds using TINs and label maps M. Li et al. 10.1016/j.isprsjprs.2019.06.003
10. 3D Point Cloud Data in Conveying Information for Local Green Factor Assessment K. Jaalama et al. 10.3390/ijgi10110762
11. Automatic point cloud coarse registration using geometric keypoint descriptors for indoor scenes M. Bueno et al. 10.1016/j.autcon.2017.06.016
12. Structure-Aware Convolution for 3D Point Cloud Classification and Segmentation L. Wang et al. 10.3390/rs12040634
13. 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
14. Geospatial Artificial Intelligence: Potentials of Machine Learning for 3D Point Clouds and Geospatial Digital Twins J. Döllner 10.1007/s41064-020-00102-3
15. Linking Points With Labels in 3D: A Review of Point Cloud Semantic Segmentation Y. Xie et al. 10.1109/MGRS.2019.2937630
16. Performance analysis of different classifiers in segmenting point cloud data N. Boslim et al. 10.1088/1742-6596/2107/1/012003
17. Evaluation Method for Shape Similarities between Archaeological Unorganized Point Cloud A. Renchin-ochir et al. 10.3756/artsci.21.136
18. 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
19. Conditional Random Field Features and Structure Assessment for Digital Terrain Modeling T. Arevalo-Ramirez & F. Auat Cheein 10.1109/ACCESS.2021.3061371
20. The Point Cloud Semantic Segmentation Method for the Ming and Qing Dynasties’ Official-Style Architecture Roof Considering the Construction Regulations Y. Dong et al. 10.3390/ijgi11040214
21. GraNet: Global relation-aware attentional network for semantic segmentation of ALS point clouds R. Huang et al. 10.1016/j.isprsjprs.2021.04.017
22. Computational Methods of Acquisition and Processing of 3D Point Cloud Data for Construction Applications Q. Wang et al. 10.1007/s11831-019-09320-4
23. Supervised Classification of Power Lines from Airborne LiDAR Data in Urban Areas Y. Wang et al. 10.3390/rs9080771
24. Classification of Stereo Images from Mobile Mapping Data Using Conditional Random Fields M. Coenen et al. 10.1007/s41064-017-0004-5
25. Sparse 3D Point Cloud Parallel Multi-Scale Feature Extraction and Dense Reconstruction with Multi-Headed Attentional Upsampling M. Wu et al. 10.3390/electronics11193157
26. Classification of 3D Digital Heritage E. Grilli & F. Remondino 10.3390/rs11070847
27. CBF-Net: An Adaptive Context Balancing and Feature Filtering Network for Point Cloud Classification Q. Zang et al. 10.1109/JSTARS.2021.3106376
28. Decision Fusion Framework for Hyperspectral Image Classification Based on Markov and Conditional Random Fields V. Andrejchenko et al. 10.3390/rs11060624
29. Feature Selection for Airbone LiDAR Point Cloud Classification M. Kuprowski & P. Drozda 10.3390/rs15030561
30. Automated Analysis of Mobile LiDAR Data for Component‐Level Damage Assessment of Building Structures during Large Coastal Storm Events Z. Zhou & J. Gong 10.1111/mice.12345
31. Machine Learning Generalisation across Different 3D Architectural Heritage E. Grilli & F. Remondino 10.3390/ijgi9060379
32. 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
33. MSNet: Multi-Scale Convolutional Network for Point Cloud Classification L. Wang et al. 10.3390/rs10040612
34. Comparative Analysis of Algorithms to Cleanse Soil Micro-Relief Point Clouds S. Ott et al. 10.3390/geomatics3040027
35. Systematic Comparison of Power Line Classification Methods from ALS and MLS Point Cloud Data Y. Wang et al. 10.3390/rs10081222
36. Classification using a three-dimensional sensor in a structured industrial environment I. Mikhailov et al. 10.1117/1.JEI.29.4.041008
37. GeoAI: a review of artificial intelligence approaches for the interpretation of complex geomatics data R. Pierdicca & M. Paolanti 10.5194/gi-11-195-2022
38. A Hierarchical Machine Learning Approach for Multi-Level and Multi-Resolution 3D Point Cloud Classification S. Teruggi et al. 10.3390/rs12162598
39. A spatial–spectral classification framework for multispectral LiDAR S. Shi et al. 10.1080/10095020.2023.2208611
40. A Hierarchical unsupervised method for power line classification from airborne LiDAR data Y. Wang et al. 10.1080/17538947.2018.1503740
41. Research on The Design Method of Auto Induction Rearview Mirror J. Zhao 10.54097/ajst.v2i3.1489
42. 3D Modeling of Urban Area Based on Oblique UAS Images—An End-to-End Pipeline V. Oniga et al. 10.3390/rs14020422
43. Incorporating Handcrafted Features into Deep Learning for Point Cloud Classification P. Hsu & Z. Zhuang 10.3390/rs12223713
44. VD-LAB: A view-decoupled network with local-global aggregation bridge for airborne laser scanning point cloud classification J. Li et al. 10.1016/j.isprsjprs.2022.01.012
45. Adaptive neighbourhood recovery method for machine learning based 3D point cloud classification J. Xue et al. 10.1080/01431161.2022.2162354
46. Hierarchical Point Cloud Transformer: A Unified Vegetation Semantic Segmentation Model for Multisource Point Clouds Based on Deep Learning X. Qiang et al. 10.1109/TGRS.2023.3336651
47. Machine learning-based segmentation of aerial LiDAR point cloud data on building roof E. Dey et al. 10.1080/22797254.2023.2210745
48. MFOC-CliqueNet: A CliqueNet-Based Optimal Combination of Multidimensional Features Classification Method for Large-Scale Laser Point Clouds L. Wang et al. 10.1155/2022/2446212
49. Classification of Large-Scale Mobile Laser Scanning Data in Urban Area with LightGBM E. Sevgen & S. Abdikan 10.3390/rs15153787
50. A Novel Octree-Based 3-D Fully Convolutional Neural Network for Point Cloud Classification in Road Environment B. Xiang et al. 10.1109/TGRS.2019.2916625
51. A Survey of Mobile Laser Scanning Applications and Key Techniques over Urban Areas . Wang et al. 10.3390/rs11131540
52. 3D Detection of Power-Transmission Lines in Point Clouds Using Random Forest Method M. Mohammadi moghaddam et al. 10.29252/jgit.8.2.75
53. Deep point embedding for urban classification using ALS point clouds: A new perspective from local to global R. Huang et al. 10.1016/j.isprsjprs.2020.02.020
54. 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