AERIAL POINT CLOUD CLASSIFICATION WITH DEEP LEARNING AND MACHINE LEARNING ALGORITHMS

9 citations as recorded by crossref.

1. Transformer based 3D semantic segmentation of urban bicycle infrastructure A. Niedermüller & M. Beeking https://doi.org/10.1080/17489725.2024.2307969
2. From the Semantic Point Cloud to Heritage-Building Information Modeling: A Semiautomatic Approach Exploiting Machine Learning V. Croce et al. https://doi.org/10.3390/rs13030461
3. Multi feature-rich synthetic colour to improve human visual perception of point clouds J. Balado et al. https://doi.org/10.1016/j.isprsjprs.2023.01.019
4. Machine learning-based segmentation of aerial LiDAR point cloud data on building roof E. Dey et al. https://doi.org/10.1080/22797254.2023.2210745
5. H-BIM and Artificial Intelligence: Classification of Architectural Heritage for Semi-Automatic Scan-to-BIM Reconstruction V. Croce et al. https://doi.org/10.3390/s23052497
6. Deep Learning for LiDAR Point Cloud Classification in Remote Sensing A. Diab et al. https://doi.org/10.3390/s22207868
7. 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. https://doi.org/10.3390/rs15081961
8. Pixel-level block classification and crack detection from 3D reconstruction models of masonry structures using convolutional neural networks D. Loverdos & V. Sarhosis https://doi.org/10.1016/j.engstruct.2024.118113
9. Generalized Extraction of Bolts, Mesh, and Rock in Tunnel Point Clouds: A Critical Comparison of Geometric Feature-Based Methods Using Random Forest and Neural Networks L. Weidner & G. Walton https://doi.org/10.3390/rs16234466