Change Detection in Point Clouds Using 3D Fractal Dimension

Autores
Casas Rosa, Juan C.; Navarro, Jose Pablo; Segura Sánchez, Rafael J.; Rueda Ruiz, Antonio J.; López Ruiz, Alfonso; Fuertes, José M.; Delrieux, Claudio Augusto; Ogayar Anguita, Carlos J.
Año de publicación
2024
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
: The management of large point clouds obtained by LiDAR sensors is an important topic in recent years due to the widespread use of this technology in a wide variety of applications and the increasing volume of data captured. One of the main applications of LIDAR systems is the study of the temporal evolution of the real environment. In open environments, it is important to know the evolution of erosive processes or landscape transformation. In the context of civil engineering and urban environments, it is useful for monitoring urban dynamics and growth, and changes during the construction of buildings or infrastructure facilities. The main problem with change detection (CD) methods is erroneous detection due to precision errors or the use of different capture devices at different times. This work presents a method to compare large point clouds, based on the study of the local fractal dimension of point clouds at multiple scales. Our method is robust in the presence of environmental and sensor factors that produce abnormal results with other methods. Furthermore, it is more stable than others in cases where there is no significant displacement of points but there is a local alteration of the structure of the point cloud. Furthermore, the precision can be adapted to the complexity and density of the point cloud. Finally, our solution is faster than other CD methods such as distance-based methods and can run at O(1) under some conditions, which is important when working with large datasets. All these improvements make the proposed method more suitable than the others to solve complex problems with LiDAR data, such as storage, time series data management, visualization, etc.
Fil: Casas Rosa, Juan C.. Universidad de Jaén; España
Fil: Navarro, Jose Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Instituto Patagónico de Ciencias Sociales y Humanas; Argentina
Fil: Segura Sánchez, Rafael J.. Universidad de Jaén; España
Fil: Rueda Ruiz, Antonio J.. Universidad de Jaén; España
Fil: López Ruiz, Alfonso. Universidad de Jaén; España
Fil: Fuertes, José M.. Universidad de Jaén; España
Fil: Delrieux, Claudio Augusto. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Ogayar Anguita, Carlos J.. Universidad de Jaén; España
Materia
LiDAR
point cloud comparison
fractal dimension
box counting
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/235431
Acceder
id CONICETDig_e7cf81df61bc600fc29b64946036660e
oai_identifier_str oai:ri.conicet.gov.ar:11336/235431
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Change Detection in Point Clouds Using 3D Fractal DimensionCasas Rosa, Juan C.Navarro, Jose PabloSegura Sánchez, Rafael J.Rueda Ruiz, Antonio J.López Ruiz, AlfonsoFuertes, José M.Delrieux, Claudio AugustoOgayar Anguita, Carlos J.LiDARpoint cloud comparisonfractal dimensionbox countinghttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1: The management of large point clouds obtained by LiDAR sensors is an important topic in recent years due to the widespread use of this technology in a wide variety of applications and the increasing volume of data captured. One of the main applications of LIDAR systems is the study of the temporal evolution of the real environment. In open environments, it is important to know the evolution of erosive processes or landscape transformation. In the context of civil engineering and urban environments, it is useful for monitoring urban dynamics and growth, and changes during the construction of buildings or infrastructure facilities. The main problem with change detection (CD) methods is erroneous detection due to precision errors or the use of different capture devices at different times. This work presents a method to compare large point clouds, based on the study of the local fractal dimension of point clouds at multiple scales. Our method is robust in the presence of environmental and sensor factors that produce abnormal results with other methods. Furthermore, it is more stable than others in cases where there is no significant displacement of points but there is a local alteration of the structure of the point cloud. Furthermore, the precision can be adapted to the complexity and density of the point cloud. Finally, our solution is faster than other CD methods such as distance-based methods and can run at O(1) under some conditions, which is important when working with large datasets. All these improvements make the proposed method more suitable than the others to solve complex problems with LiDAR data, such as storage, time series data management, visualization, etc.Fil: Casas Rosa, Juan C.. Universidad de Jaén; EspañaFil: Navarro, Jose Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Instituto Patagónico de Ciencias Sociales y Humanas; ArgentinaFil: Segura Sánchez, Rafael J.. Universidad de Jaén; EspañaFil: Rueda Ruiz, Antonio J.. Universidad de Jaén; EspañaFil: López Ruiz, Alfonso. Universidad de Jaén; EspañaFil: Fuertes, José M.. Universidad de Jaén; EspañaFil: Delrieux, Claudio Augusto. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Ogayar Anguita, Carlos J.. Universidad de Jaén; EspañaMDPI2024-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/235431Casas Rosa, Juan C.; Navarro, Jose Pablo; Segura Sánchez, Rafael J.; Rueda Ruiz, Antonio J.; López Ruiz, Alfonso; et al.; Change Detection in Point Clouds Using 3D Fractal Dimension; MDPI; Remote Sensing; 16; 6; 3-2024; 1-182072-4292CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2072-4292/16/6/1054info:eu-repo/semantics/altIdentifier/doi/10.3390/rs16061054info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-10T13:10:50Zoai:ri.conicet.gov.ar:11336/235431instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-10 13:10:51.119CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Change Detection in Point Clouds Using 3D Fractal Dimension
title Change Detection in Point Clouds Using 3D Fractal Dimension
spellingShingle Change Detection in Point Clouds Using 3D Fractal Dimension
Casas Rosa, Juan C.
LiDAR
point cloud comparison
fractal dimension
box counting
title_short Change Detection in Point Clouds Using 3D Fractal Dimension
title_full Change Detection in Point Clouds Using 3D Fractal Dimension
title_fullStr Change Detection in Point Clouds Using 3D Fractal Dimension
title_full_unstemmed Change Detection in Point Clouds Using 3D Fractal Dimension
title_sort Change Detection in Point Clouds Using 3D Fractal Dimension
dc.creator.none.fl_str_mv Casas Rosa, Juan C.
Navarro, Jose Pablo
Segura Sánchez, Rafael J.
Rueda Ruiz, Antonio J.
López Ruiz, Alfonso
Fuertes, José M.
Delrieux, Claudio Augusto
Ogayar Anguita, Carlos J.
author Casas Rosa, Juan C.
author_facet Casas Rosa, Juan C.
Navarro, Jose Pablo
Segura Sánchez, Rafael J.
Rueda Ruiz, Antonio J.
López Ruiz, Alfonso
Fuertes, José M.
Delrieux, Claudio Augusto
Ogayar Anguita, Carlos J.
author_role author
author2 Navarro, Jose Pablo
Segura Sánchez, Rafael J.
Rueda Ruiz, Antonio J.
López Ruiz, Alfonso
Fuertes, José M.
Delrieux, Claudio Augusto
Ogayar Anguita, Carlos J.
author2_role author
author
author
author
author
author
author
dc.subject.none.fl_str_mv LiDAR
point cloud comparison
fractal dimension
box counting
topic LiDAR
point cloud comparison
fractal dimension
box counting
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv : The management of large point clouds obtained by LiDAR sensors is an important topic in recent years due to the widespread use of this technology in a wide variety of applications and the increasing volume of data captured. One of the main applications of LIDAR systems is the study of the temporal evolution of the real environment. In open environments, it is important to know the evolution of erosive processes or landscape transformation. In the context of civil engineering and urban environments, it is useful for monitoring urban dynamics and growth, and changes during the construction of buildings or infrastructure facilities. The main problem with change detection (CD) methods is erroneous detection due to precision errors or the use of different capture devices at different times. This work presents a method to compare large point clouds, based on the study of the local fractal dimension of point clouds at multiple scales. Our method is robust in the presence of environmental and sensor factors that produce abnormal results with other methods. Furthermore, it is more stable than others in cases where there is no significant displacement of points but there is a local alteration of the structure of the point cloud. Furthermore, the precision can be adapted to the complexity and density of the point cloud. Finally, our solution is faster than other CD methods such as distance-based methods and can run at O(1) under some conditions, which is important when working with large datasets. All these improvements make the proposed method more suitable than the others to solve complex problems with LiDAR data, such as storage, time series data management, visualization, etc.
Fil: Casas Rosa, Juan C.. Universidad de Jaén; España
Fil: Navarro, Jose Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Instituto Patagónico de Ciencias Sociales y Humanas; Argentina
Fil: Segura Sánchez, Rafael J.. Universidad de Jaén; España
Fil: Rueda Ruiz, Antonio J.. Universidad de Jaén; España
Fil: López Ruiz, Alfonso. Universidad de Jaén; España
Fil: Fuertes, José M.. Universidad de Jaén; España
Fil: Delrieux, Claudio Augusto. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Ogayar Anguita, Carlos J.. Universidad de Jaén; España
description : The management of large point clouds obtained by LiDAR sensors is an important topic in recent years due to the widespread use of this technology in a wide variety of applications and the increasing volume of data captured. One of the main applications of LIDAR systems is the study of the temporal evolution of the real environment. In open environments, it is important to know the evolution of erosive processes or landscape transformation. In the context of civil engineering and urban environments, it is useful for monitoring urban dynamics and growth, and changes during the construction of buildings or infrastructure facilities. The main problem with change detection (CD) methods is erroneous detection due to precision errors or the use of different capture devices at different times. This work presents a method to compare large point clouds, based on the study of the local fractal dimension of point clouds at multiple scales. Our method is robust in the presence of environmental and sensor factors that produce abnormal results with other methods. Furthermore, it is more stable than others in cases where there is no significant displacement of points but there is a local alteration of the structure of the point cloud. Furthermore, the precision can be adapted to the complexity and density of the point cloud. Finally, our solution is faster than other CD methods such as distance-based methods and can run at O(1) under some conditions, which is important when working with large datasets. All these improvements make the proposed method more suitable than the others to solve complex problems with LiDAR data, such as storage, time series data management, visualization, etc.
publishDate 2024
dc.date.none.fl_str_mv 2024-03
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/235431
Casas Rosa, Juan C.; Navarro, Jose Pablo; Segura Sánchez, Rafael J.; Rueda Ruiz, Antonio J.; López Ruiz, Alfonso; et al.; Change Detection in Point Clouds Using 3D Fractal Dimension; MDPI; Remote Sensing; 16; 6; 3-2024; 1-18
2072-4292
CONICET Digital
CONICET
url http://hdl.handle.net/11336/235431
identifier_str_mv Casas Rosa, Juan C.; Navarro, Jose Pablo; Segura Sánchez, Rafael J.; Rueda Ruiz, Antonio J.; López Ruiz, Alfonso; et al.; Change Detection in Point Clouds Using 3D Fractal Dimension; MDPI; Remote Sensing; 16; 6; 3-2024; 1-18
2072-4292
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2072-4292/16/6/1054
info:eu-repo/semantics/altIdentifier/doi/10.3390/rs16061054
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 12.993085

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