FULLY CONVOLUTIONAL NETWORKS FOR GROUND CLASSIFICATION FROM LIDAR POINT CLOUDS

22 citations as recorded by crossref.

1. Virtual Disassembling of Historical Edifices: Experiments and Assessments of an Automatic Approach for Classifying Multi-Scalar Point Clouds into Architectural Elements A. Murtiyoso & P. Grussenmeyer https://doi.org/10.3390/s20082161
2. Big Data Geospatial Processing for Massive Aerial LiDAR Datasets D. Deibe et al. https://doi.org/10.3390/rs12040719
3. Mapping the Topographic Features of Mining-Related Valley Fills Using Mask R-CNN Deep Learning and Digital Elevation Data A. Maxwell et al. https://doi.org/10.3390/rs12030547
4. Classification of ALS Point Clouds Using End-to-End Deep Learning L. Winiwarter et al. https://doi.org/10.1007/s41064-019-00073-0
5. SC-CNN: LiDAR point cloud filtering CNN under slope and copula correlation constraint R. Chen et al. https://doi.org/10.1016/j.isprsjprs.2024.05.012
6. Vertical slice equal sampling and transformer network for point cloud ground filtering W. Wen et al. https://doi.org/10.1080/01431161.2024.2368927
7. A Point-Based Fully Convolutional Neural Network for Airborne LiDAR Ground Point Filtering in Forested Environments S. Jin et al. https://doi.org/10.1109/JSTARS.2020.3008477
8. Automatic Workflow for Roof Extraction and Generation of 3D CityGML Models from Low-Cost UAV Image-Derived Point Clouds A. Murtiyoso et al. https://doi.org/10.3390/ijgi9120743
9. Correction of Low Vegetation Impact on UAV-Derived Point Cloud Heights With U-Net Networks W. Gruszczynski et al. https://doi.org/10.1109/TGRS.2021.3057272
10. RETRACTED ARTICLE: Flexible big data approach for geospatial analysis R. Malik & M. Nishi https://doi.org/10.1007/s12652-021-02925-3
11. Advanced Fully Convolutional Networks for Agricultural Field Boundary Detection A. Taravat et al. https://doi.org/10.3390/rs13040722
12. A deep neural network based on U-Net for classifying airborne LiDAR data F. Uray & A. Varlık https://doi.org/10.51489/tuzal.1778332
13. A Multi-scale Convolutional Neural Network for Precise Digital Terrain Model Extraction from Digital Surface Models A. Al-Fugara et al. https://doi.org/10.1007/s11220-025-00694-3
14. Searching for Viking Age Fortresses with Automatic Landscape Classification and Feature Detection D. Stott et al. https://doi.org/10.3390/rs11161881
15. Full-Waveform Airborne LiDAR Data Classification Using Convolutional Neural Networks S. Zorzi et al. https://doi.org/10.1109/TGRS.2019.2919472
16. Ground and Multi-Class Classification of Airborne Laser Scanner Point Clouds Using Fully Convolutional Networks A. Rizaldy et al. https://doi.org/10.3390/rs10111723
17. A new weakly supervised approach for ALS point cloud semantic segmentation P. Wang & W. Yao https://doi.org/10.1016/j.isprsjprs.2022.04.016
18. Feature selection for airborne LiDAR data filtering: a mutual information method with Parzon window optimization Z. Cai et al. https://doi.org/10.1080/15481603.2019.1695406
19. A deep learning approach to identify smoke plumes in satellite imagery in near-real time for health risk communication A. Larsen et al. https://doi.org/10.1038/s41370-020-0246-y
20. A Dual Attention Neural Network for Airborne LiDAR Point Cloud Semantic Segmentation K. Zhang et al. https://doi.org/10.1109/TGRS.2022.3201902
21. A New Approach for Ground Filtering of Airborne Laser Scanning Data Using PointNet++ Z. Akbulut et al. https://doi.org/10.1007/s12524-023-01794-x
22. Enhancing the Accuracy of Digital Terrain Model in Complex Coastal Landscape: Case Study at Jupiter Inlet Lighthouse ONA, Florida N. Manikkavasagam et al. https://doi.org/10.1061/JSUED2.SUENG-1599