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3D UAS-SfM Point Cloud Classification in Urban Terrestrial Environment

EasyChair Preprint no. 10655

2 pagesDate: August 2, 2023


The use of lidar technologies for point cloud acquisition is financially costly. However, photogrammetry using unmanned aerial systems (UAS) imagery and structure-from-motion (SfM) techniques have proven a practical and costeffective way to collect point cloud data (Liu and Boehm, 2015). SfM uses two-dimensional (2D) images to produce high-quality three-dimensional (3D) point clouds. Classified point cloud data is useful in environmental modelling, cultural heritage preservation and navigation applications (Grilli et al., 2017; Roynard et al., 2018; Croce et al., 2021). This study focuses on classifying a 3D UAS-SfM point cloud of a heterogeneous urban environment into three land cover categories: ground, high vegetation and buildings.

Keyphrases: Classification, Mapping, point cloud, Structure from Motion, Unmanned Aerial Systems

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Simiso Ntuli and Angus Forbes and Mayshree Singh and Mulemwa Akombelwa},
  title = {3D UAS-SfM Point Cloud Classification in Urban Terrestrial Environment},
  howpublished = {EasyChair Preprint no. 10655},

  year = {EasyChair, 2023}}
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