Automatic Building Change Detection Using Multi-Temporal Airborne Lidar Data

Detalhes bibliográficos
Autor(a) principal: Dos Santos, R. C. [UNESP]
Data de Publicação: 2020
Outros Autores: Galo, M. [UNESP], Carrilho, A. C. [UNESP], Pessoa, G. G. [UNESP], De Oliveira, R. A.R. [UNESP]
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1109/LAGIRS48042.2020.9165628
http://hdl.handle.net/11449/206569
Resumo: The automatic detection of building changes is an essential process for urban area monitoring, urban planning, and database update. In this context, 3D information derived from multi-temporal airborne LiDAR scanning is one effective alternative. Despite several works in the literature, the separation of change areas in building and non-building remains a challenge. In this sense, it is proposed a new method for building change detection, having as the main contribution the use of height entropy concept to identify the building change areas. The experiments were performed considering multi-temporal airborne LiDAR data from 2012 and 2014, both with average density around 5 points/m2. Qualitative and quantitative analyses indicate that the proposed method is robust in building change detection, having the potential to identify small changes (larger than 20 m2). In general, the change detection method presented average completeness and correctness around 97% and 71%, respectively.
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spelling Automatic Building Change Detection Using Multi-Temporal Airborne Lidar DataThe automatic detection of building changes is an essential process for urban area monitoring, urban planning, and database update. In this context, 3D information derived from multi-temporal airborne LiDAR scanning is one effective alternative. Despite several works in the literature, the separation of change areas in building and non-building remains a challenge. In this sense, it is proposed a new method for building change detection, having as the main contribution the use of height entropy concept to identify the building change areas. The experiments were performed considering multi-temporal airborne LiDAR data from 2012 and 2014, both with average density around 5 points/m2. Qualitative and quantitative analyses indicate that the proposed method is robust in building change detection, having the potential to identify small changes (larger than 20 m2). In general, the change detection method presented average completeness and correctness around 97% and 71%, respectively.São Paulo State University - UNESP Graduate Program in Cartographic SciencesSão Paulo State University - UNESP Dept. of CartographySão Paulo State University - UNESP Graduate Program in Cartographic SciencesSão Paulo State University - UNESP Dept. of CartographyUniversidade Estadual Paulista (Unesp)Dos Santos, R. C. [UNESP]Galo, M. [UNESP]Carrilho, A. C. [UNESP]Pessoa, G. G. [UNESP]De Oliveira, R. A.R. [UNESP]2021-06-25T10:34:29Z2021-06-25T10:34:29Z2020-03-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject54-59http://dx.doi.org/10.1109/LAGIRS48042.2020.91656282020 IEEE Latin American GRSS and ISPRS Remote Sensing Conference, LAGIRS 2020 - Proceedings, p. 54-59.http://hdl.handle.net/11449/20656910.1109/LAGIRS48042.2020.91656282-s2.0-85091633352Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng2020 IEEE Latin American GRSS and ISPRS Remote Sensing Conference, LAGIRS 2020 - Proceedingsinfo:eu-repo/semantics/openAccess2024-06-18T15:02:41Zoai:repositorio.unesp.br:11449/206569Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T22:45:24.632617Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Automatic Building Change Detection Using Multi-Temporal Airborne Lidar Data
title Automatic Building Change Detection Using Multi-Temporal Airborne Lidar Data
spellingShingle Automatic Building Change Detection Using Multi-Temporal Airborne Lidar Data
Dos Santos, R. C. [UNESP]
title_short Automatic Building Change Detection Using Multi-Temporal Airborne Lidar Data
title_full Automatic Building Change Detection Using Multi-Temporal Airborne Lidar Data
title_fullStr Automatic Building Change Detection Using Multi-Temporal Airborne Lidar Data
title_full_unstemmed Automatic Building Change Detection Using Multi-Temporal Airborne Lidar Data
title_sort Automatic Building Change Detection Using Multi-Temporal Airborne Lidar Data
author Dos Santos, R. C. [UNESP]
author_facet Dos Santos, R. C. [UNESP]
Galo, M. [UNESP]
Carrilho, A. C. [UNESP]
Pessoa, G. G. [UNESP]
De Oliveira, R. A.R. [UNESP]
author_role author
author2 Galo, M. [UNESP]
Carrilho, A. C. [UNESP]
Pessoa, G. G. [UNESP]
De Oliveira, R. A.R. [UNESP]
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
dc.contributor.author.fl_str_mv Dos Santos, R. C. [UNESP]
Galo, M. [UNESP]
Carrilho, A. C. [UNESP]
Pessoa, G. G. [UNESP]
De Oliveira, R. A.R. [UNESP]
description The automatic detection of building changes is an essential process for urban area monitoring, urban planning, and database update. In this context, 3D information derived from multi-temporal airborne LiDAR scanning is one effective alternative. Despite several works in the literature, the separation of change areas in building and non-building remains a challenge. In this sense, it is proposed a new method for building change detection, having as the main contribution the use of height entropy concept to identify the building change areas. The experiments were performed considering multi-temporal airborne LiDAR data from 2012 and 2014, both with average density around 5 points/m2. Qualitative and quantitative analyses indicate that the proposed method is robust in building change detection, having the potential to identify small changes (larger than 20 m2). In general, the change detection method presented average completeness and correctness around 97% and 71%, respectively.
publishDate 2020
dc.date.none.fl_str_mv 2020-03-01
2021-06-25T10:34:29Z
2021-06-25T10:34:29Z
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/LAGIRS48042.2020.9165628
2020 IEEE Latin American GRSS and ISPRS Remote Sensing Conference, LAGIRS 2020 - Proceedings, p. 54-59.
http://hdl.handle.net/11449/206569
10.1109/LAGIRS48042.2020.9165628
2-s2.0-85091633352
url http://dx.doi.org/10.1109/LAGIRS48042.2020.9165628
http://hdl.handle.net/11449/206569
identifier_str_mv 2020 IEEE Latin American GRSS and ISPRS Remote Sensing Conference, LAGIRS 2020 - Proceedings, p. 54-59.
10.1109/LAGIRS48042.2020.9165628
2-s2.0-85091633352
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2020 IEEE Latin American GRSS and ISPRS Remote Sensing Conference, LAGIRS 2020 - Proceedings
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 54-59
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)
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institution UNESP
reponame_str Repositório Institucional da UNESP
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repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
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