Automatic Building Boundary Extraction from Airborne LiDAR Data Robust to Density Variation
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/LGRS.2020.3031397 http://hdl.handle.net/11449/230181 |
Resumo: | The alpha-shape ( $\alpha $ -shape) concept, which has its origin in computational geometry, is usually applied in building boundary extraction from airborne LiDAR data. However, the results depend on the appropriate choice of the parameter $\alpha $. Despite several studies in the literature, the adaptive choice of the parameter $\alpha $ persists a challenge in boundary extraction, especially when abrupt density variations occur. To overcome this limitation, this letter proposes a new approach combining five estimation strategies. In the proposed method, these strategies are tested sequentially, prioritizing the one that provides greater level of details. The experiments were conducted considering buildings with different characteristics, which were selected from two LiDAR data sets with the average point densities of 12 points/m2 and 4 points/m2. The obtained results, presenting $\boldsymbol {F} _{{\text {score}}}$ and PoLiS around 98% and 0.32 m, respectively, indicate the robustness of the proposed method even when abrupt density variation occurs. |
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Repositório Institucional da UNESP |
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Automatic Building Boundary Extraction from Airborne LiDAR Data Robust to Density VariationAirborne LiDAR dataalpha-shape algorithmbuilding boundary extractionpoint density variationThe alpha-shape ( $\alpha $ -shape) concept, which has its origin in computational geometry, is usually applied in building boundary extraction from airborne LiDAR data. However, the results depend on the appropriate choice of the parameter $\alpha $. Despite several studies in the literature, the adaptive choice of the parameter $\alpha $ persists a challenge in boundary extraction, especially when abrupt density variations occur. To overcome this limitation, this letter proposes a new approach combining five estimation strategies. In the proposed method, these strategies are tested sequentially, prioritizing the one that provides greater level of details. The experiments were conducted considering buildings with different characteristics, which were selected from two LiDAR data sets with the average point densities of 12 points/m2 and 4 points/m2. The obtained results, presenting $\boldsymbol {F} _{{\text {score}}}$ and PoLiS around 98% and 0.32 m, respectively, indicate the robustness of the proposed method even when abrupt density variation occurs.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Department of Cartography and Graduate Program on Cartographic Sciences (PPGCC) São Paulo State University, SPGraduate Program on Cartographic Sciences (PPGCC) São Paulo State University, SPDepartment of Cartography São Paulo State University, SPDepartment of Cartography and Graduate Program on Cartographic Sciences (PPGCC) São Paulo State University, SPGraduate Program on Cartographic Sciences (PPGCC) São Paulo State University, SPDepartment of Cartography São Paulo State University, SPFAPESP: 2016/12167-5Universidade Estadual Paulista (UNESP)Dos Santos, Renato Cesar [UNESP]Pessoa, Guilherme Gomes [UNESP]Carrilho, Andre Caceres [UNESP]Galo, Mauricio [UNESP]2022-04-29T08:38:17Z2022-04-29T08:38:17Z2022-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1109/LGRS.2020.3031397IEEE Geoscience and Remote Sensing Letters, v. 19.1558-05711545-598Xhttp://hdl.handle.net/11449/23018110.1109/LGRS.2020.30313972-s2.0-85122407017Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengIEEE Geoscience and Remote Sensing Lettersinfo:eu-repo/semantics/openAccess2024-06-18T15:01:26Zoai:repositorio.unesp.br:11449/230181Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-06-18T15:01:26Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Automatic Building Boundary Extraction from Airborne LiDAR Data Robust to Density Variation |
title |
Automatic Building Boundary Extraction from Airborne LiDAR Data Robust to Density Variation |
spellingShingle |
Automatic Building Boundary Extraction from Airborne LiDAR Data Robust to Density Variation Dos Santos, Renato Cesar [UNESP] Airborne LiDAR data alpha-shape algorithm building boundary extraction point density variation |
title_short |
Automatic Building Boundary Extraction from Airborne LiDAR Data Robust to Density Variation |
title_full |
Automatic Building Boundary Extraction from Airborne LiDAR Data Robust to Density Variation |
title_fullStr |
Automatic Building Boundary Extraction from Airborne LiDAR Data Robust to Density Variation |
title_full_unstemmed |
Automatic Building Boundary Extraction from Airborne LiDAR Data Robust to Density Variation |
title_sort |
Automatic Building Boundary Extraction from Airborne LiDAR Data Robust to Density Variation |
author |
Dos Santos, Renato Cesar [UNESP] |
author_facet |
Dos Santos, Renato Cesar [UNESP] Pessoa, Guilherme Gomes [UNESP] Carrilho, Andre Caceres [UNESP] Galo, Mauricio [UNESP] |
author_role |
author |
author2 |
Pessoa, Guilherme Gomes [UNESP] Carrilho, Andre Caceres [UNESP] Galo, Mauricio [UNESP] |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) |
dc.contributor.author.fl_str_mv |
Dos Santos, Renato Cesar [UNESP] Pessoa, Guilherme Gomes [UNESP] Carrilho, Andre Caceres [UNESP] Galo, Mauricio [UNESP] |
dc.subject.por.fl_str_mv |
Airborne LiDAR data alpha-shape algorithm building boundary extraction point density variation |
topic |
Airborne LiDAR data alpha-shape algorithm building boundary extraction point density variation |
description |
The alpha-shape ( $\alpha $ -shape) concept, which has its origin in computational geometry, is usually applied in building boundary extraction from airborne LiDAR data. However, the results depend on the appropriate choice of the parameter $\alpha $. Despite several studies in the literature, the adaptive choice of the parameter $\alpha $ persists a challenge in boundary extraction, especially when abrupt density variations occur. To overcome this limitation, this letter proposes a new approach combining five estimation strategies. In the proposed method, these strategies are tested sequentially, prioritizing the one that provides greater level of details. The experiments were conducted considering buildings with different characteristics, which were selected from two LiDAR data sets with the average point densities of 12 points/m2 and 4 points/m2. The obtained results, presenting $\boldsymbol {F} _{{\text {score}}}$ and PoLiS around 98% and 0.32 m, respectively, indicate the robustness of the proposed method even when abrupt density variation occurs. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-04-29T08:38:17Z 2022-04-29T08:38:17Z 2022-01-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1109/LGRS.2020.3031397 IEEE Geoscience and Remote Sensing Letters, v. 19. 1558-0571 1545-598X http://hdl.handle.net/11449/230181 10.1109/LGRS.2020.3031397 2-s2.0-85122407017 |
url |
http://dx.doi.org/10.1109/LGRS.2020.3031397 http://hdl.handle.net/11449/230181 |
identifier_str_mv |
IEEE Geoscience and Remote Sensing Letters, v. 19. 1558-0571 1545-598X 10.1109/LGRS.2020.3031397 2-s2.0-85122407017 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
IEEE Geoscience and Remote Sensing Letters |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
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_ |
1803649607048101888 |