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

60 citations as recorded by crossref.

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25. Integrating LiDAR and multispectral remote sensing data for understanding wetland-flood dynamics: a literature review M. Anokye & L. Hashemi-Beni
26. 3D reconstruction of buildings from LiDAR data considering various types of roof structures P. Pahlavani et al.
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28. Semiautomatic Workflow for Accurate LiDAR-Derived DSM Retrieval in Aquatic Scenarios via Water Surface Mapping E. Piaser et al.
29. Construction of a Semantic Segmentation Network for the Overhead Catenary System Point Cloud Based on Multi-Scale Feature Fusion T. Xu et al.
30. Comparison of heuristic and deep learning-based methods for ground classification from aerial point clouds M. Soilán et al.
31. Automating Parameter Learning for Classifying Terrestrial LiDAR Point Cloud Using 2D Land Cover Maps C. Feng & Z. Guo
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33. A Semantic Segmentation Network for ALS Point Clouds Considering Geographic Location and Feature Interaction Z. Wang et al.
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35. Contour Extraction in Buildings in Airborne LiDAR Point Clouds Using Multiscale Local Geometric Descriptors and Visual Analytics J. Sreevalsan-Nair et al.
36. Exploring Semantic Information Extraction From Different Data Forms in 3D Point Cloud Semantic Segmentation A. Zhang et al.
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40. Ground and Multi-Class Classification of Airborne Laser Scanner Point Clouds Using Fully Convolutional Networks A. Rizaldy et al.
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42. Building Extraction from Airborne LiDAR Data Based on Multi-Constraints Graph Segmentation Z. Hui et al.
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49. Multigranularity Semantic Labeling of Point Clouds for the Measurement of the Rail Tanker Component With Structure Modeling C. Qi & J. Yin
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51. Robust and fast reconstruction of complex roofs with active sampling from 3D point clouds Y. Dehbi et al.
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57. Improving trajectory estimation using 3D city models and kinematic point clouds L. Lucks et al.
58. A Comparison of Deep Learning Methods for Airborne Lidar Point Clouds Classification N. Li et al.
59. DANCE-NET: Density-aware convolution networks with context encoding for airborne LiDAR point cloud classification X. Li et al.
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