Urban tree surveying using aerial UAV images and machine learning algorithms

Autores
D'amato, Juan Pablo; Rinaldi, Pablo Rafael; Boroni, Gustavo Adolfo
Año de publicación
2025
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In this work, a novel approach to surveying urban trees based on automatic detecting from images captured using unmanned aerial vehicles (UAVs) is presented. Such a method is a cost-effective alternative to traditional measurement techniques. Through autonomous flights, UAVs capture detailed aerial imagery of urban areas, which is then processed to generate high-resolution raster images and elevation models. Machine learning algorithms are then applied to these images to identify trees, refining the detection process by eliminating false positives and estimating tree heights. Dealing with challenges such as flight time limitations and the irregularity of urban trees, the method achieves great accuracy in tree identification, not only covering the tree detection but also the separation between sidewalk and block interior trees, the estimation of height among other important data. Although the methodology does not cover all aspects of tree surveying, such as trunk health or diameter, it serves as a complementary tool to ground survey systems.
Fil: D'amato, Juan Pablo. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Científicas. Grupo de Plasmas Densos Magnetizados; Argentina
Fil: Rinaldi, Pablo Rafael. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Científicas. Grupo de Plasmas Densos Magnetizados; Argentina
Fil: Boroni, Gustavo Adolfo. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Científicas. Grupo de Plasmas Densos Magnetizados; Argentina
Materia
machine learning,
UAV
urban studies
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/274584
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Urban tree surveying using aerial UAV images and machine learning algorithmsD'amato, Juan PabloRinaldi, Pablo RafaelBoroni, Gustavo Adolfomachine learning,UAVurban studieshttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1In this work, a novel approach to surveying urban trees based on automatic detecting from images captured using unmanned aerial vehicles (UAVs) is presented. Such a method is a cost-effective alternative to traditional measurement techniques. Through autonomous flights, UAVs capture detailed aerial imagery of urban areas, which is then processed to generate high-resolution raster images and elevation models. Machine learning algorithms are then applied to these images to identify trees, refining the detection process by eliminating false positives and estimating tree heights. Dealing with challenges such as flight time limitations and the irregularity of urban trees, the method achieves great accuracy in tree identification, not only covering the tree detection but also the separation between sidewalk and block interior trees, the estimation of height among other important data. Although the methodology does not cover all aspects of tree surveying, such as trunk health or diameter, it serves as a complementary tool to ground survey systems.Fil: D'amato, Juan Pablo. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Científicas. Grupo de Plasmas Densos Magnetizados; ArgentinaFil: Rinaldi, Pablo Rafael. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Científicas. Grupo de Plasmas Densos Magnetizados; ArgentinaFil: Boroni, Gustavo Adolfo. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Científicas. Grupo de Plasmas Densos Magnetizados; ArgentinaInformation and Technology Management Association2025-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/274584D'amato, Juan Pablo; Rinaldi, Pablo Rafael; Boroni, Gustavo Adolfo; Urban tree surveying using aerial UAV images and machine learning algorithms; Information and Technology Management Association; World Journal of Information Systems; 1; 3; 3-2025; 71-823051-6420CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.17013/wjis.v1i3.20info:eu-repo/semantics/altIdentifier/url/https://wjis.org/index.php/wjis/article/view/20info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-11-05T10:40:25Zoai:ri.conicet.gov.ar:11336/274584instacron: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:34982025-11-05 10:40:25.795CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Urban tree surveying using aerial UAV images and machine learning algorithms
title Urban tree surveying using aerial UAV images and machine learning algorithms
spellingShingle Urban tree surveying using aerial UAV images and machine learning algorithms
D'amato, Juan Pablo
machine learning,
UAV
urban studies
title_short Urban tree surveying using aerial UAV images and machine learning algorithms
title_full Urban tree surveying using aerial UAV images and machine learning algorithms
title_fullStr Urban tree surveying using aerial UAV images and machine learning algorithms
title_full_unstemmed Urban tree surveying using aerial UAV images and machine learning algorithms
title_sort Urban tree surveying using aerial UAV images and machine learning algorithms
dc.creator.none.fl_str_mv D'amato, Juan Pablo
Rinaldi, Pablo Rafael
Boroni, Gustavo Adolfo
author D'amato, Juan Pablo
author_facet D'amato, Juan Pablo
Rinaldi, Pablo Rafael
Boroni, Gustavo Adolfo
author_role author
author2 Rinaldi, Pablo Rafael
Boroni, Gustavo Adolfo
author2_role author
author
dc.subject.none.fl_str_mv machine learning,
UAV
urban studies
topic machine learning,
UAV
urban studies
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv In this work, a novel approach to surveying urban trees based on automatic detecting from images captured using unmanned aerial vehicles (UAVs) is presented. Such a method is a cost-effective alternative to traditional measurement techniques. Through autonomous flights, UAVs capture detailed aerial imagery of urban areas, which is then processed to generate high-resolution raster images and elevation models. Machine learning algorithms are then applied to these images to identify trees, refining the detection process by eliminating false positives and estimating tree heights. Dealing with challenges such as flight time limitations and the irregularity of urban trees, the method achieves great accuracy in tree identification, not only covering the tree detection but also the separation between sidewalk and block interior trees, the estimation of height among other important data. Although the methodology does not cover all aspects of tree surveying, such as trunk health or diameter, it serves as a complementary tool to ground survey systems.
Fil: D'amato, Juan Pablo. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Científicas. Grupo de Plasmas Densos Magnetizados; Argentina
Fil: Rinaldi, Pablo Rafael. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Científicas. Grupo de Plasmas Densos Magnetizados; Argentina
Fil: Boroni, Gustavo Adolfo. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Científicas. Grupo de Plasmas Densos Magnetizados; Argentina
description In this work, a novel approach to surveying urban trees based on automatic detecting from images captured using unmanned aerial vehicles (UAVs) is presented. Such a method is a cost-effective alternative to traditional measurement techniques. Through autonomous flights, UAVs capture detailed aerial imagery of urban areas, which is then processed to generate high-resolution raster images and elevation models. Machine learning algorithms are then applied to these images to identify trees, refining the detection process by eliminating false positives and estimating tree heights. Dealing with challenges such as flight time limitations and the irregularity of urban trees, the method achieves great accuracy in tree identification, not only covering the tree detection but also the separation between sidewalk and block interior trees, the estimation of height among other important data. Although the methodology does not cover all aspects of tree surveying, such as trunk health or diameter, it serves as a complementary tool to ground survey systems.
publishDate 2025
dc.date.none.fl_str_mv 2025-03
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/11336/274584
D'amato, Juan Pablo; Rinaldi, Pablo Rafael; Boroni, Gustavo Adolfo; Urban tree surveying using aerial UAV images and machine learning algorithms; Information and Technology Management Association; World Journal of Information Systems; 1; 3; 3-2025; 71-82
3051-6420
CONICET Digital
CONICET
url http://hdl.handle.net/11336/274584
identifier_str_mv D'amato, Juan Pablo; Rinaldi, Pablo Rafael; Boroni, Gustavo Adolfo; Urban tree surveying using aerial UAV images and machine learning algorithms; Information and Technology Management Association; World Journal of Information Systems; 1; 3; 3-2025; 71-82
3051-6420
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.17013/wjis.v1i3.20
info:eu-repo/semantics/altIdentifier/url/https://wjis.org/index.php/wjis/article/view/20
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Information and Technology Management Association
publisher.none.fl_str_mv Information and Technology Management Association
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 13.087074

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