MODELING OF ROOFS FROM POINT CLOUDS USING GENETIC ALGORITHMS
Autor(a) principal: | |
---|---|
Data de Publicação: | 2020 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Boletim de Ciências Geodésicas |
Texto Completo: | https://revistas.ufpr.br/bcg/article/view/73144 |
Resumo: | Building roof extraction has been studied for more than thirty years and it generates models that provide important information for many applications, especially urban planning. The present work aimed to model roofs only from point clouds using genetic algorithms (GAs) to develop a more automatized and efficient method. For this, firstly, an algorithm for edge detection was developed. Experiments were performed with simulated and real point clouds, obtained by LIDAR. In the experiments with simulated point clouds, three types of point clouds with different complexities were created, and the effects of noise and scan line spacing on the results were evaluated. For the experiments with real point clouds, five roofs were chosen as examples, each with a different characteristic. GAs were used to select, among the points identified during edge detection, the so-called ‘significant points’, those which are essential to the accurate reconstruction of the roof model. These points were then used to generate the models, which were assessed qualitatively and quantitatively. Such evaluations showed that the use of GAs proved to be efficient for the modeling of roofs, as the model geometry was satisfactory, the error was within an acceptable range, and the computational effort was clearly reduced. |
id |
UFPR-2_6af8e3128ae7b5606075a0ea5c4a0ebd |
---|---|
oai_identifier_str |
oai:revistas.ufpr.br:article/73144 |
network_acronym_str |
UFPR-2 |
network_name_str |
Boletim de Ciências Geodésicas |
repository_id_str |
|
spelling |
MODELING OF ROOFS FROM POINT CLOUDS USING GENETIC ALGORITHMSGeociências; Ciências da terra.Roof modeling; Genetic algorithms; Point clouds; Light Detection And Ranging - LIDAR.Building roof extraction has been studied for more than thirty years and it generates models that provide important information for many applications, especially urban planning. The present work aimed to model roofs only from point clouds using genetic algorithms (GAs) to develop a more automatized and efficient method. For this, firstly, an algorithm for edge detection was developed. Experiments were performed with simulated and real point clouds, obtained by LIDAR. In the experiments with simulated point clouds, three types of point clouds with different complexities were created, and the effects of noise and scan line spacing on the results were evaluated. For the experiments with real point clouds, five roofs were chosen as examples, each with a different characteristic. GAs were used to select, among the points identified during edge detection, the so-called ‘significant points’, those which are essential to the accurate reconstruction of the roof model. These points were then used to generate the models, which were assessed qualitatively and quantitatively. Such evaluations showed that the use of GAs proved to be efficient for the modeling of roofs, as the model geometry was satisfactory, the error was within an acceptable range, and the computational effort was clearly reduced.Boletim de Ciências GeodésicasBulletin of Geodetic SciencesSabariego, NatáliaCenteno, Jorge Antonio Silva2020-04-24info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://revistas.ufpr.br/bcg/article/view/73144Boletim de Ciências Geodésicas; Vol 26, No 1 (2020)Bulletin of Geodetic Sciences; Vol 26, No 1 (2020)1982-21701413-4853reponame:Boletim de Ciências Geodésicasinstname:Universidade Federal do Paraná (UFPR)instacron:UFPRenghttps://revistas.ufpr.br/bcg/article/view/73144/40583Copyright (c) 2020 Natália Sabariego, Jorge Antonio Silva Centenohttp://creativecommons.org/licenses/by-nc/4.0info:eu-repo/semantics/openAccess2020-04-24T17:34:11Zoai:revistas.ufpr.br:article/73144Revistahttps://revistas.ufpr.br/bcgPUBhttps://revistas.ufpr.br/bcg/oaiqdalmolin@ufpr.br|| danielsantos@ufpr.br||qdalmolin@ufpr.br|| danielsantos@ufpr.br1982-21701413-4853opendoar:2020-04-24T17:34:11Boletim de Ciências Geodésicas - Universidade Federal do Paraná (UFPR)false |
dc.title.none.fl_str_mv |
MODELING OF ROOFS FROM POINT CLOUDS USING GENETIC ALGORITHMS |
title |
MODELING OF ROOFS FROM POINT CLOUDS USING GENETIC ALGORITHMS |
spellingShingle |
MODELING OF ROOFS FROM POINT CLOUDS USING GENETIC ALGORITHMS Sabariego, Natália Geociências; Ciências da terra. Roof modeling; Genetic algorithms; Point clouds; Light Detection And Ranging - LIDAR. |
title_short |
MODELING OF ROOFS FROM POINT CLOUDS USING GENETIC ALGORITHMS |
title_full |
MODELING OF ROOFS FROM POINT CLOUDS USING GENETIC ALGORITHMS |
title_fullStr |
MODELING OF ROOFS FROM POINT CLOUDS USING GENETIC ALGORITHMS |
title_full_unstemmed |
MODELING OF ROOFS FROM POINT CLOUDS USING GENETIC ALGORITHMS |
title_sort |
MODELING OF ROOFS FROM POINT CLOUDS USING GENETIC ALGORITHMS |
author |
Sabariego, Natália |
author_facet |
Sabariego, Natália Centeno, Jorge Antonio Silva |
author_role |
author |
author2 |
Centeno, Jorge Antonio Silva |
author2_role |
author |
dc.contributor.none.fl_str_mv |
|
dc.contributor.author.fl_str_mv |
Sabariego, Natália Centeno, Jorge Antonio Silva |
dc.subject.none.fl_str_mv |
|
dc.subject.por.fl_str_mv |
Geociências; Ciências da terra. Roof modeling; Genetic algorithms; Point clouds; Light Detection And Ranging - LIDAR. |
topic |
Geociências; Ciências da terra. Roof modeling; Genetic algorithms; Point clouds; Light Detection And Ranging - LIDAR. |
description |
Building roof extraction has been studied for more than thirty years and it generates models that provide important information for many applications, especially urban planning. The present work aimed to model roofs only from point clouds using genetic algorithms (GAs) to develop a more automatized and efficient method. For this, firstly, an algorithm for edge detection was developed. Experiments were performed with simulated and real point clouds, obtained by LIDAR. In the experiments with simulated point clouds, three types of point clouds with different complexities were created, and the effects of noise and scan line spacing on the results were evaluated. For the experiments with real point clouds, five roofs were chosen as examples, each with a different characteristic. GAs were used to select, among the points identified during edge detection, the so-called ‘significant points’, those which are essential to the accurate reconstruction of the roof model. These points were then used to generate the models, which were assessed qualitatively and quantitatively. Such evaluations showed that the use of GAs proved to be efficient for the modeling of roofs, as the model geometry was satisfactory, the error was within an acceptable range, and the computational effort was clearly reduced. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-04-24 |
dc.type.none.fl_str_mv |
|
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://revistas.ufpr.br/bcg/article/view/73144 |
url |
https://revistas.ufpr.br/bcg/article/view/73144 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://revistas.ufpr.br/bcg/article/view/73144/40583 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2020 Natália Sabariego, Jorge Antonio Silva Centeno http://creativecommons.org/licenses/by-nc/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2020 Natália Sabariego, Jorge Antonio Silva Centeno http://creativecommons.org/licenses/by-nc/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Boletim de Ciências Geodésicas Bulletin of Geodetic Sciences |
publisher.none.fl_str_mv |
Boletim de Ciências Geodésicas Bulletin of Geodetic Sciences |
dc.source.none.fl_str_mv |
Boletim de Ciências Geodésicas; Vol 26, No 1 (2020) Bulletin of Geodetic Sciences; Vol 26, No 1 (2020) 1982-2170 1413-4853 reponame:Boletim de Ciências Geodésicas instname:Universidade Federal do Paraná (UFPR) instacron:UFPR |
instname_str |
Universidade Federal do Paraná (UFPR) |
instacron_str |
UFPR |
institution |
UFPR |
reponame_str |
Boletim de Ciências Geodésicas |
collection |
Boletim de Ciências Geodésicas |
repository.name.fl_str_mv |
Boletim de Ciências Geodésicas - Universidade Federal do Paraná (UFPR) |
repository.mail.fl_str_mv |
qdalmolin@ufpr.br|| danielsantos@ufpr.br||qdalmolin@ufpr.br|| danielsantos@ufpr.br |
_version_ |
1799771720005976064 |