FULLY CONVOLUTIONAL NETWORKS FOR GROUND CLASSIFICATION FROM LIDAR POINT CLOUDS

22 citations as recorded by crossref.

1. Automated ground filtering of LiDAR and UAS point clouds with metaheuristics V. Yilmaz 10.1016/j.optlastec.2020.106890
2. 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 10.3390/s20082161
3. Big Data Geospatial Processing for Massive Aerial LiDAR Datasets D. Deibe et al. 10.3390/rs12040719
4. 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
5. Classification of ALS Point Clouds Using End-to-End Deep Learning L. Winiwarter et al. 10.1007/s41064-019-00073-0
6. SC-CNN: LiDAR point cloud filtering CNN under slope and copula correlation constraint R. Chen et al. 10.1016/j.isprsjprs.2024.05.012
7. Vertical slice equal sampling and transformer network for point cloud ground filtering W. Wen et al. 10.1080/01431161.2024.2368927
8. A Point-Based Fully Convolutional Neural Network for Airborne LiDAR Ground Point Filtering in Forested Environments S. Jin et al. 10.1109/JSTARS.2020.3008477
9. Automatic Workflow for Roof Extraction and Generation of 3D CityGML Models from Low-Cost UAV Image-Derived Point Clouds A. Murtiyoso et al. 10.3390/ijgi9120743
10. Correction of Low Vegetation Impact on UAV-Derived Point Cloud Heights With U-Net Networks W. Gruszczynski et al. 10.1109/TGRS.2021.3057272
11. RETRACTED ARTICLE: Flexible big data approach for geospatial analysis R. Malik & M. Nishi 10.1007/s12652-021-02925-3
12. Advanced Fully Convolutional Networks for Agricultural Field Boundary Detection A. Taravat et al. 10.3390/rs13040722
13. Searching for Viking Age Fortresses with Automatic Landscape Classification and Feature Detection D. Stott et al. 10.3390/rs11161881
14. Full-Waveform Airborne LiDAR Data Classification Using Convolutional Neural Networks S. Zorzi et al. 10.1109/TGRS.2019.2919472
15. Ground and Multi-Class Classification of Airborne Laser Scanner Point Clouds Using Fully Convolutional Networks A. Rizaldy et al. 10.3390/rs10111723
16. A new weakly supervised approach for ALS point cloud semantic segmentation P. Wang & W. Yao 10.1016/j.isprsjprs.2022.04.016
17. Feature selection for airborne LiDAR data filtering: a mutual information method with Parzon window optimization Z. Cai et al. 10.1080/15481603.2019.1695406
18. A deep learning approach to identify smoke plumes in satellite imagery in near-real time for health risk communication A. Larsen et al. 10.1038/s41370-020-0246-y
19. A Dual Attention Neural Network for Airborne LiDAR Point Cloud Semantic Segmentation K. Zhang et al. 10.1109/TGRS.2022.3201902
20. A New Approach for Ground Filtering of Airborne Laser Scanning Data Using PointNet++ Z. Akbulut et al. 10.1007/s12524-023-01794-x
21. Enhancing the Accuracy of Digital Terrain Model in Complex Coastal Landscape: Case Study at Jupiter Inlet Lighthouse ONA, Florida N. Manikkavasagam et al. 10.1061/JSUED2.SUENG-1599
22. 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