Outdoor LiDAR Point Cloud Building Segmentation: Progress and Challenge

Ahmad Gamal, Ario Yudo Husodo, Grafika Jati, Machmud R. Alhamidi, M. Anwar Ma'Sum, Ronni Ardhianto, Wisnu Jatmiko

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

The demand for 3D modeling using LiDAR as the primary source for observing, planning, and managing urban areas has increased. Using LiDAR data improves the accuracy of the modeling so that it can be used for policy determination and infrastructure planning. Various kinds of research on LiDAR data have been carried out, one of which is indoor and outdoor LiDAR segmentation. For outdoor cases, LiDAR data can be obtained from two points of view, namely ground view and aerial view. In this paper, we discuss the advancements and challenges of LiDAR 3D modeling in building segmentation that we have carried out. We collect LiDAR data with unmanned aerial vehicles. We use several algorithms such as PointNet and the Dynamic Graph Convolutional Neural Network variations to group structures from LiDAR data. The result is that the proposed method can segment buildings, surfaces, and vegetation well. The average accuracy produced for the Kupang and Depok datasets reaches 70%-80%.

Original languageEnglish
Title of host publication2021 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665442640
DOIs
Publication statusPublished - 2021
Event13th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2021 - Depok, Indonesia
Duration: 23 Oct 202126 Oct 2021

Publication series

Name2021 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2021

Conference

Conference13th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2021
Country/TerritoryIndonesia
CityDepok
Period23/10/2126/10/21

Keywords

  • 3D Modeling
  • Building Segmentation
  • Dynamic Graph Convolutional Neural Network
  • LiDAR
  • PointNet

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