GROUND POINT FILTERING FROM AIRBORNE LIDAR POINT CLOUDS USING DEEP LEARNING: A PRELIMINARY STUDY

10 citations as recorded by crossref.

1. A new technical pathway for extracting high accuracy surface deformation information in coal mining areas using UAV LiDAR data: An example from the Yushen mining area in western China Q. Yang et al. 10.1016/j.measurement.2023.113220
2. FWNet: Semantic Segmentation for Full-Waveform LiDAR Data Using Deep Learning T. Shinohara et al. 10.3390/s20123568
3. A New Approach for Ground Filtering of Airborne Laser Scanning Data Using PointNet++ Z. Akbulut et al. 10.1007/s12524-023-01794-x
4. Potential of airborne LiDAR data for terrain parameters extraction M. Sharma et al. 10.1016/j.quaint.2020.07.039
5. Investigation of the performances of advanced image classification‐based ground filtering approaches for digital terrain model generation V. Yilmaz 10.1002/cpe.6219
6. Correction of Low Vegetation Impact on UAV-Derived Point Cloud Heights With U-Net Networks W. Gruszczynski et al. 10.1109/TGRS.2021.3057272
7. Lake Level Reconstructed From DEM-Based Virtual Station: Comparison of Multisource DEMs With Laser Altimetry and UAV-LiDAR Measurements P. Zhan et al. 10.1109/LGRS.2021.3086582
8. Comparison of heuristic and deep learning-based methods for ground classification from aerial point clouds M. Soilán et al. 10.1080/17538947.2019.1663948
9. Mapping the Topographic Features of Mining-Related Valley Fills Using Mask R-CNN Deep Learning and Digital Elevation Data A. Maxwell et al. 10.3390/rs12030547
10. Towards intelligent ground filtering of large-scale topographic point clouds: A comprehensive survey N. Qin et al. 10.1016/j.jag.2023.103566