AN EFFICIENT DEEP LEARNING APPROACH FOR GROUND POINT FILTERING IN AERIAL LASER SCANNING POINT CLOUDS

12 citations as recorded by crossref.

1. An Improved Adaptive Grid-Based Progressive Triangulated Irregular Network Densification Algorithm for Filtering Airborne LiDAR Data J. Zheng et al. 10.3390/rs16203846
2. Deep learning for filtering the ground from ALS point clouds: A dataset, evaluations and issues N. Qin et al. 10.1016/j.isprsjprs.2023.06.005
3. Towards intelligent ground filtering of large-scale topographic point clouds: A comprehensive survey N. Qin et al. 10.1016/j.jag.2023.103566
4. MDC-Net: a multi-directional constrained and prior assisted neural network for wood and leaf separation from terrestrial laser scanning W. Dai et al. 10.1080/17538947.2023.2198261
5. A discordance analysis in manual labelling of urban mobile laser scanning data used for deep learning based semantic segmentation S. González-Collazo et al. 10.1016/j.eswa.2023.120672
6. Deep learning-based hyperspectral microscopic imaging for cholangiocarcinoma detection and classification S. Kumar et al. 10.1364/OPTCON.527576
7. Learning geometric complexes for 3D shape classification P. Kudeshia et al. 10.1016/j.cag.2024.104119
8. Review of Automatic Processing of Topography and Surface Feature Identification LiDAR Data Using Machine Learning Techniques Z. Gharineiat et al. 10.3390/rs14194685
9. A New Approach for Ground Filtering of Airborne Laser Scanning Data Using PointNet++ Z. Akbulut et al. 10.1007/s12524-023-01794-x
10. Potential damage area detection of bridges based on single-temporal point cloud X. Liu et al. 10.1088/1361-6501/ad9041
11. Real-time ground filtering algorithm of cloud points acquired using Terrestrial Laser Scanner (TLS) N. Diaz et al. 10.1016/j.jag.2021.102629
12. A Method for the Automatic Extraction of Support Devices in an Overhead Catenary System Based on MLS Point Clouds S. Zhang et al. 10.3390/rs14235915