Automatic Building Change Detection Using Multi-Temporal Airborne Lidar Data
Autor(a) principal: | |
---|---|
Data de Publicação: | 2020 |
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/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. |
id |
UNSP_6882b9d3833d733a7b024bac1fd2c0db |
---|---|
oai_identifier_str |
oai:repositorio.unesp.br:11449/206569 |
network_acronym_str |
UNSP |
network_name_str |
Repositório Institucional da UNESP |
repository_id_str |
2946 |
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) |
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_ |
1808129459410173952 |