CLASSIFICATION OF AERIAL POINT CLOUDS WITH DEEP LEARNING

11 citations as recorded by crossref.

1. Classification of UAV point clouds by random forest machine learning algorithm M. ZEYBEK
2. A Bayesian framework for 3D point cloud filtering H. AbdulJabbar Sadeq
3. A Simple Semantic-Based Data Storage Layout for Querying Point Clouds S. El-Mahgary et al.
4. Two-Dimensional Shape Analysis of Complex Geometry Based on Photogrammetric Models of Iconostases I. Đurić et al.
5. An Efficient and General Framework for Aerial Point Cloud Classification in Urban Scenarios E. Özdemir et al.
6. Fully Automatic Point Cloud Analysis for Powerline Corridor Mapping C. Nardinocchi et al.
7. Investigating Prior-Level Fusion Approaches for Enriched Semantic Segmentation of Urban LiDAR Point Clouds Z. Ballouch et al.
8. IAMS (Interior-Anchored Mean-Shift) Algorithm for Supervoxel Segmentation of Airborne LiDAR Roof Points H. Zhou et al.
9. GeoAI: a review of artificial intelligence approaches for the interpretation of complex geomatics data R. Pierdicca & M. Paolanti
10. Deep Learning-Based Classification of Large-Scale Airborne LiDAR Point Cloud M. Turgeon-Pelchat et al.
11. A comprehensive review on application of drone, virtual reality and augmented reality with their application in dragline excavation monitoring in surface mines P. Singh et al.