SUBMANIFOLD SPARSE CONVOLUTIONAL NETWORKS FOR SEMANTIC SEGMENTATION OF LARGE-SCALE ALS POINT CLOUDS

23 citations as recorded by crossref.

1. 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
2. Weakly supervised semantic segmentation of airborne laser scanning point clouds Y. Lin et al. 10.1016/j.isprsjprs.2022.03.001
3. Deep Ground Filtering of Large-Scale ALS Point Clouds via Iterative Sequential Ground Prediction H. Dai et al. 10.3390/rs15040961
4. Towards intelligent ground filtering of large-scale topographic point clouds: A comprehensive survey N. Qin et al. 10.1016/j.jag.2023.103566
5. Classification of ALS Point Clouds Using End-to-End Deep Learning L. Winiwarter et al. 10.1007/s41064-019-00073-0
6. Large-Scale ALS Point Cloud Segmentation via Projection-Based Context Embedding H. Dai et al. 10.1109/TGRS.2024.3392267
7. Incorporating Handcrafted Features into Deep Learning for Point Cloud Classification P. Hsu & Z. Zhuang 10.3390/rs12223713
8. Human‐in‐the‐loop development of spatially adaptive ground point filtering pipelines—An archaeological case study M. Doneus et al. 10.1002/arp.1873
9. 3DMASC: Accessible, explainable 3D point clouds classification. Application to bi-spectral topo-bathymetric lidar data M. Letard et al. 10.1016/j.isprsjprs.2023.11.022
10. Building a Fully-Automatized Active Learning Framework for the Semantic Segmentation of Geospatial 3D Point Clouds M. Kölle et al. 10.1007/s41064-024-00281-3
11. 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
12. Airborne Laser Scanning Point Cloud Classification Using the DGCNN Deep Learning Method E. Widyaningrum et al. 10.3390/rs13050859
13. HAVANA: Hard Negative Sample-Aware Self-Supervised Contrastive Learning for Airborne Laser Scanning Point Cloud Semantic Segmentation Y. Zhang et al. 10.3390/rs16030485
14. Classification of airborne 3D point clouds regarding separation of vegetation in complex environments D. Bulatov et al. 10.1364/AO.422973
15. BathyNet: A Deep Neural Network for Water Depth Mapping from Multispectral Aerial Images G. Mandlburger et al. 10.1007/s41064-021-00142-3
16. Terrain-Net: A Highly-Efficient, Parameter-Free, and Easy-to-Use Deep Neural Network for Ground Filtering of UAV LiDAR Data in Forested Environments B. Li et al. 10.3390/rs14225798
17. A Comparison of Deep Learning Methods for Airborne Lidar Point Clouds Classification N. Li et al. 10.1109/JSTARS.2021.3091389
18. Juggling with representations: On the information transfer between imagery, point clouds, and meshes for multi-modal semantics D. Laupheimer & N. Haala 10.1016/j.isprsjprs.2021.03.007
19. Individual Tree Detection in Urban ALS Point Clouds with 3D Convolutional Networks S. Schmohl et al. 10.3390/rs14061317
20. Archaeological Ground Point Filtering of Airborne Laser Scan Derived Point-Clouds in a Difficult Mediterranean Environment M. Doneus et al. 10.5334/jcaa.44
21. CBF-Net: An Adaptive Context Balancing and Feature Filtering Network for Point Cloud Classification Q. Zang et al. 10.1109/JSTARS.2021.3106376
22. A Dual Attention Neural Network for Airborne LiDAR Point Cloud Semantic Segmentation K. Zhang et al. 10.1109/TGRS.2022.3201902
23. A Hybrid Framework Combining Data-Driven and Catenary-Based Methods for Wide-Area Powerline Sag Estimation Y. Wu et al. 10.3390/en15145245