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

11 citations as recorded by crossref.

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2. Intelligent processing of UAV remote sensing data for building high-precision DEMs in complex terrain: A case study of Loess Plateau in China Q. Yang et al.
3. End-to-End High-Quality DEM Generation and Ground Filtering From ALS Point Clouds J. Xu et al.
4. Point-SCT: A Multiscale Spatial Convolution-Swin Transformer Network for Point Cloud Ground Filtering in Complex Mountainous Terrains J. Li et al.
5. A New Approach for Ground Filtering of Airborne Laser Scanning Data Using PointNet++ Z. Akbulut et al.
6. Investigation of the performances of advanced image classification‐based ground filtering approaches for digital terrain model generation V. Yilmaz
7. Correction of Low Vegetation Impact on UAV-Derived Point Cloud Heights With U-Net Networks W. Gruszczynski et al.
8. Lake Level Reconstructed From DEM-Based Virtual Station: Comparison of Multisource DEMs With Laser Altimetry and UAV-LiDAR Measurements P. Zhan et al.
9. Comparison of heuristic and deep learning-based methods for ground classification from aerial point clouds M. Soilán et al.
10. Mapping the Topographic Features of Mining-Related Valley Fills Using Mask R-CNN Deep Learning and Digital Elevation Data A. Maxwell et al.
11. Towards intelligent ground filtering of large-scale topographic point clouds: A comprehensive survey N. Qin et al.