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

Z. Hui; Y. Hu; Y.Y. Ziggah

doi:10.3990/2.408

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 proceeding › Conference contribution › Academic › peer-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 language	English
Title of host publication	Proceedings of GEOBIA 2016 : Solutions and synergies, 14-16 September 2016, Enschede, Netherlands
Editors	N. Kerle, M. Gerke, S. Lefevre
Place of Publication	Enschede
Publisher	University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC)
Number of pages	2
ISBN (Print)	978-90-365-4201-2
DOIs	https://doi.org/10.3990/2.408
Publication status	Published - 14 Sep 2016
Externally published	Yes
Event	6th 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 Sep 2016 → 16 Sep 2016 Conference number: 6 https://www.geobia2016.com/

Conference

Conference	6th International Conference on Geographic Object-Based Image Analysis, GEOBIA 2016
Abbreviated title	GEOBIA
Country/Territory	Netherlands
City	Enschede
Period	14/09/16 → 16/09/16
Internet address	https://www.geobia2016.com/

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Hui-Automatic Building Extraction from Airborne LiDAR Point Cloud Based-29Final published version, 64.1 KB

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GEOBIA 2016 : Solutions and Synergies., 14-16 September 2016, University of Twente Faculty of Geo-Information and Earth Observation (ITC): open access e-book
Kerle, N. (ed.), Gerke, M. (ed.) & Lefèvre, S. (ed.), 2016, Enschede: University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC).
Research output: Book/Report › Book › Academic

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Hui, Z., Hu, Y., & Ziggah, Y. Y. (2016). Automatic building extraction from airborne LiDAR point cloud based on mean shift segmentation. In N. Kerle, M. Gerke, & S. Lefevre (Eds.), Proceedings of GEOBIA 2016 : Solutions and synergies, 14-16 September 2016, Enschede, Netherlands University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC). https://doi.org/10.3990/2.408

Hui, Z. ; Hu, Y. ; Ziggah, Y.Y. / Automatic building extraction from airborne LiDAR point cloud based on mean shift segmentation. Proceedings of GEOBIA 2016 : Solutions and synergies, 14-16 September 2016, Enschede, Netherlands. editor / N. Kerle ; M. Gerke ; S. Lefevre. Enschede : University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC), 2016.

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title = "Automatic building extraction from airborne LiDAR point cloud based on mean shift segmentation",

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.",

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publisher = "University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC)",

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Hui, Z, Hu, Y & Ziggah, YY 2016, Automatic building extraction from airborne LiDAR point cloud based on mean shift segmentation. in N Kerle, M Gerke & S Lefevre (eds), Proceedings of GEOBIA 2016 : Solutions and synergies, 14-16 September 2016, Enschede, Netherlands. University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC), Enschede, 6th International Conference on Geographic Object-Based Image Analysis, GEOBIA 2016, Enschede, Netherlands, 14/09/16. https://doi.org/10.3990/2.408

Automatic building extraction from airborne LiDAR point cloud based on mean shift segmentation. / Hui, Z.; Hu, Y.; Ziggah, Y.Y.

Proceedings of GEOBIA 2016 : Solutions and synergies, 14-16 September 2016, Enschede, Netherlands. ed. / N. Kerle; M. Gerke; S. Lefevre. Enschede : University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC), 2016.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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Y1 - 2016/9/14

N2 - 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.

AB - 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.

U2 - 10.3990/2.408

DO - 10.3990/2.408

M3 - Conference contribution

SN - 978-90-365-4201-2

BT - Proceedings of GEOBIA 2016 : Solutions and synergies, 14-16 September 2016, Enschede, Netherlands

A2 - Kerle, N.

A2 - Gerke, M.

A2 - Lefevre, S.

PB - University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC)

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Y2 - 14 September 2016 through 16 September 2016

ER -

Hui Z, Hu Y, Ziggah YY. Automatic building extraction from airborne LiDAR point cloud based on mean shift segmentation. In Kerle N, Gerke M, Lefevre S, editors, Proceedings of GEOBIA 2016 : Solutions and synergies, 14-16 September 2016, Enschede, Netherlands. Enschede: University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC). 2016 doi: 10.3990/2.408

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

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Research output

GEOBIA 2016 : Solutions and Synergies., 14-16 September 2016, University of Twente Faculty of Geo-Information and Earth Observation (ITC): open access e-book

Cite this