POTENTIAL OF FULL WAVEFORM AIRBORNE LASER SCANNING DATA FOR URBAN AREA CLASSIFICATION – TRANSFER OF CLASSIFICATION APPROACHES BETWEEN MISSIONS

7 citations as recorded by crossref.

1. A Comparison of Deep Learning Methods for Airborne Lidar Point Clouds Classification N. Li et al. 10.1109/JSTARS.2021.3091389
2. Airborne LiDAR point cloud classification using PointNet++ network with full neighborhood features X. Nong et al. 10.1371/journal.pone.0280346
3. Automatic Airborne Laser Scanning Data Quality Control Procedure for Environmental Studies B. Kraszewski et al. 10.2478/ffp-2020-0030
4. Integration of UAS and Backpack-LiDAR to Estimate Aboveground Biomass of Picea crassifolia Forest in Eastern Qinghai, China J. Ali et al. 10.3390/rs17040681
5. Use of SfM-MVS approach to nadir and oblique images generated throught aerial cameras to build 2.5D map and 3D models in urban areas M. Pepe et al. 10.1080/10106049.2019.1700558
6. Improving LiDAR classification accuracy by contextual label smoothing in post-processing N. Li et al. 10.1016/j.isprsjprs.2018.11.022
7. Full-Waveform Airborne Laser Scanning in Vegetation Studies—A Review of Point Cloud and Waveform Features for Tree Species Classification K. Koenig & B. Höfle 10.3390/f7090198