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dc.contributor.author
Casas Rosa, Juan C.  
dc.contributor.author
Navarro, Jose Pablo  
dc.contributor.author
Segura Sánchez, Rafael J.  
dc.contributor.author
Rueda Ruiz, Antonio J.  
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López Ruiz, Alfonso  
dc.contributor.author
Fuertes, José M.  
dc.contributor.author
Delrieux, Claudio Augusto  
dc.contributor.author
Ogayar Anguita, Carlos J.  
dc.date.available
2024-05-15T15:55:27Z  
dc.date.issued
2024-03  
dc.identifier.citation
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  
dc.identifier.issn
2072-4292  
dc.identifier.uri
http://hdl.handle.net/11336/235431  
dc.description.abstract
: 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.  
dc.format
application/pdf  
dc.language.iso
eng  
dc.publisher
MDPI  
dc.rights
info:eu-repo/semantics/openAccess  
dc.rights.uri
https://creativecommons.org/licenses/by/2.5/ar/  
dc.subject
LiDAR  
dc.subject
point cloud comparison  
dc.subject
fractal dimension  
dc.subject
box counting  
dc.subject.classification
Ciencias de la Computación  
dc.subject.classification
Ciencias de la Computación e Información  
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS  
dc.title
Change Detection in Point Clouds Using 3D Fractal Dimension  
dc.type
info:eu-repo/semantics/article  
dc.type
info:ar-repo/semantics/artículo  
dc.type
info:eu-repo/semantics/publishedVersion  
dc.date.updated
2024-04-16T09:46:46Z  
dc.journal.volume
16  
dc.journal.number
6  
dc.journal.pagination
1-18  
dc.journal.pais
Suiza  
dc.description.fil
Fil: Casas Rosa, Juan C.. Universidad de Jaén; España  
dc.description.fil
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  
dc.description.fil
Fil: Segura Sánchez, Rafael J.. Universidad de Jaén; España  
dc.description.fil
Fil: Rueda Ruiz, Antonio J.. Universidad de Jaén; España  
dc.description.fil
Fil: López Ruiz, Alfonso. Universidad de Jaén; España  
dc.description.fil
Fil: Fuertes, José M.. Universidad de Jaén; España  
dc.description.fil
Fil: Delrieux, Claudio Augusto. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina  
dc.description.fil
Fil: Ogayar Anguita, Carlos J.. Universidad de Jaén; España  
dc.journal.title
Remote Sensing  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2072-4292/16/6/1054  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.3390/rs16061054