AERIAL POINT CLOUD CLASSIFICATION WITH DEEP LEARNING AND MACHINE LEARNING ALGORITHMS

13 citations as recorded by crossref.

1. An Efficient and General Framework for Aerial Point Cloud Classification in Urban Scenarios E. Özdemir et al. 10.3390/rs13101985
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5. Extracting Urban Road Footprints from Airborne LiDAR Point Clouds with PointNet++ and Two-Step Post-Processing H. Ma et al. 10.3390/rs14030789
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7. H-BIM and Artificial Intelligence: Classification of Architectural Heritage for Semi-Automatic Scan-to-BIM Reconstruction V. Croce et al. 10.3390/s23052497
8. Rapid Reconstruction of 3D Structural Model Based on Interactive Graph Cuts S. Han et al. 10.3390/buildings12010022
9. 3D Point Cloud Data in Conveying Information for Local Green Factor Assessment K. Jaalama et al. 10.3390/ijgi10110762
10. Deep Learning for LiDAR Point Cloud Classification in Remote Sensing A. Diab et al. 10.3390/s22207868
11. Machine Learning and Deep Learning for the Built Heritage Analysis: Laser Scanning and UAV-Based Surveying Applications on a Complex Spatial Grid Structure D. Billi et al. 10.3390/rs15081961
12. Machine Learning-Based Supervised Classification of Point Clouds Using Multiscale Geometric Features M. Atik et al. 10.3390/ijgi10030187
13. Digital Deforestation: Comparing Automated Approaches to the Production of Digital Terrain Models (DTMs) in Agisoft Metashape M. Howland et al. 10.3390/quat5010005