RANSAC-based segmentation for building roof face detection in LiDAR point cloud
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/IGARSS.2018.8518502 http://hdl.handle.net/11449/228675 |
Resumo: | This work proposes a method for segmenting the roof planes of buildings in Light Detection and Ranging (LiDAR) data. First, a preprocessing of the point cloud is performed to separate the points belonging to each building. The RANdom SAmple Consensus (RANSAC) method is then used in each building region to identify sets of coplanar points belonging to the roof faces. Finally, planar segments representing the same roof face are connected to minimize the fragmentation that may occur in the previous step. This requires the use of techniques for analyzing the continuity of adjacent planar segments. Although several thresholds are required, they can be predetermined or adapted, thus avoiding their modification by an operator in each application of the method. The results show that the proposed method functions appropriately, rarely failing in regions affected by local structures such as trees and antennas. Consequently, average rates higher than 90% were obtained for completeness and correction. |
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Repositório Institucional da UNESP |
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RANSAC-based segmentation for building roof face detection in LiDAR point cloudLiDARRANSACRoof segmentationThis work proposes a method for segmenting the roof planes of buildings in Light Detection and Ranging (LiDAR) data. First, a preprocessing of the point cloud is performed to separate the points belonging to each building. The RANdom SAmple Consensus (RANSAC) method is then used in each building region to identify sets of coplanar points belonging to the roof faces. Finally, planar segments representing the same roof face are connected to minimize the fragmentation that may occur in the previous step. This requires the use of techniques for analyzing the continuity of adjacent planar segments. Although several thresholds are required, they can be predetermined or adapted, thus avoiding their modification by an operator in each application of the method. The results show that the proposed method functions appropriately, rarely failing in regions affected by local structures such as trees and antennas. Consequently, average rates higher than 90% were obtained for completeness and correction.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Dept. of Cartography São Paulo State University, 305 R. Roberto SimonsenDept. of Cartography São Paulo State University, 305 R. Roberto SimonsenFAPESP: 2013/16452-8Universidade Estadual Paulista (UNESP)Dal Poz, Aluir Porfirio [UNESP]Yano, Michelle Sayuri [UNESP]2022-04-29T08:27:59Z2022-04-29T08:27:59Z2018-10-31info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject1276-1279http://dx.doi.org/10.1109/IGARSS.2018.8518502International Geoscience and Remote Sensing Symposium (IGARSS), v. 2018-July, p. 1276-1279.http://hdl.handle.net/11449/22867510.1109/IGARSS.2018.85185022-s2.0-85064164611Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengInternational Geoscience and Remote Sensing Symposium (IGARSS)info:eu-repo/semantics/openAccess2022-04-29T08:27:59Zoai:repositorio.unesp.br:11449/228675Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462022-04-29T08:27:59Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
RANSAC-based segmentation for building roof face detection in LiDAR point cloud |
title |
RANSAC-based segmentation for building roof face detection in LiDAR point cloud |
spellingShingle |
RANSAC-based segmentation for building roof face detection in LiDAR point cloud Dal Poz, Aluir Porfirio [UNESP] LiDAR RANSAC Roof segmentation |
title_short |
RANSAC-based segmentation for building roof face detection in LiDAR point cloud |
title_full |
RANSAC-based segmentation for building roof face detection in LiDAR point cloud |
title_fullStr |
RANSAC-based segmentation for building roof face detection in LiDAR point cloud |
title_full_unstemmed |
RANSAC-based segmentation for building roof face detection in LiDAR point cloud |
title_sort |
RANSAC-based segmentation for building roof face detection in LiDAR point cloud |
author |
Dal Poz, Aluir Porfirio [UNESP] |
author_facet |
Dal Poz, Aluir Porfirio [UNESP] Yano, Michelle Sayuri [UNESP] |
author_role |
author |
author2 |
Yano, Michelle Sayuri [UNESP] |
author2_role |
author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) |
dc.contributor.author.fl_str_mv |
Dal Poz, Aluir Porfirio [UNESP] Yano, Michelle Sayuri [UNESP] |
dc.subject.por.fl_str_mv |
LiDAR RANSAC Roof segmentation |
topic |
LiDAR RANSAC Roof segmentation |
description |
This work proposes a method for segmenting the roof planes of buildings in Light Detection and Ranging (LiDAR) data. First, a preprocessing of the point cloud is performed to separate the points belonging to each building. The RANdom SAmple Consensus (RANSAC) method is then used in each building region to identify sets of coplanar points belonging to the roof faces. Finally, planar segments representing the same roof face are connected to minimize the fragmentation that may occur in the previous step. This requires the use of techniques for analyzing the continuity of adjacent planar segments. Although several thresholds are required, they can be predetermined or adapted, thus avoiding their modification by an operator in each application of the method. The results show that the proposed method functions appropriately, rarely failing in regions affected by local structures such as trees and antennas. Consequently, average rates higher than 90% were obtained for completeness and correction. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-10-31 2022-04-29T08:27:59Z 2022-04-29T08:27:59Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1109/IGARSS.2018.8518502 International Geoscience and Remote Sensing Symposium (IGARSS), v. 2018-July, p. 1276-1279. http://hdl.handle.net/11449/228675 10.1109/IGARSS.2018.8518502 2-s2.0-85064164611 |
url |
http://dx.doi.org/10.1109/IGARSS.2018.8518502 http://hdl.handle.net/11449/228675 |
identifier_str_mv |
International Geoscience and Remote Sensing Symposium (IGARSS), v. 2018-July, p. 1276-1279. 10.1109/IGARSS.2018.8518502 2-s2.0-85064164611 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
International Geoscience and Remote Sensing Symposium (IGARSS) |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
1276-1279 |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1799964707760635904 |