Automatic building extraction from airborne LiDAR point cloud based on mean shift segmentation

Z. Hui*, Y. Hu, Y.Y. Ziggah

*Corresponding author for this work

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

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Abstract

Building extraction is an important part for smart city construction. This paper proposes a novel method for automatic building extraction from airborne LiDAR point cloud. In the present study, filtering was first applied to point cloud, which could help obtain elevated points for generating the DTM. The building-candidate points were then obtained by setting a threshold from the DTM. To distinguish the tree points from building points, three constraints, namely, area constraint, point density constraint and root mean square error constraint were applied to the building-candidate points. By comparing with the reference data generated manually, the evaluation result shows that the proposed method could yield a good performance.
Original languageEnglish
Title of host publicationProceedings of GEOBIA 2016 : Solutions and synergies, 14-16 September 2016, Enschede, Netherlands
EditorsN. Kerle, M. Gerke, S. Lefevre
Place of PublicationEnschede
PublisherUniversity of Twente, Faculty of Geo-Information Science and Earth Observation (ITC)
Number of pages2
ISBN (Print)978-90-365-4201-2
DOIs
Publication statusPublished - 14 Sept 2016
Externally publishedYes
Event6th International Conference on Geographic Object-Based Image Analysis, GEOBIA 2016: Solutions & Synergies - University of Twente Faculty of Geo-Information and Earth Observation (ITC), Enschede, Netherlands
Duration: 14 Sept 201616 Sept 2016
Conference number: 6
https://www.geobia2016.com/

Conference

Conference6th International Conference on Geographic Object-Based Image Analysis, GEOBIA 2016
Abbreviated titleGEOBIA
Country/TerritoryNetherlands
CityEnschede
Period14/09/1616/09/16
Internet address

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