Streamlining the study of the Tierra del Fuego forest through the use of deep learning

Autores
Viera, Leonel; González, Federico; Soler, Rosina; Romano, Lucas; Feierherd, Guillermo Eugenio
Año de publicación
2019
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Understanding plant-herbivorous relationships allows to optimize the way to manage and protect natural spaces. In this paper the study of this relationship in the ñire forests (Nothofagus antarctica) of the province of Tierra del Fuego (Argentina) is approached. Using trap cameras to monitor such interaction offers the opportunity to quickly collect large amounts of data. However, to take advantage of its potential, a large investment in trained personnel to analyze and filter the images of interest is required. The present work seeks to establish a path to significantly reduce this obstacle using the advances of machine and deep learning in the recognition of objects from images.
XVII Workshop Computación Gráfica, Imágenes y Visualización.
Red de Universidades con Carreras en Informática
Materia
Ciencias Informáticas
Machine learning
Deep learning
Computer vision
Trap cameras
Forests
Image recognition
Ñire
Antarctic nothofagus
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/91024
Acceder
id SEDICI_d4bfd59ec698a2f20a1e188e9fc89b20
oai_identifier_str oai:sedici.unlp.edu.ar:10915/91024
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Streamlining the study of the Tierra del Fuego forest through the use of deep learningViera, LeonelGonzález, FedericoSoler, RosinaRomano, LucasFeierherd, Guillermo EugenioCiencias InformáticasMachine learningDeep learningComputer visionTrap camerasForestsImage recognitionÑireAntarctic nothofagusUnderstanding plant-herbivorous relationships allows to optimize the way to manage and protect natural spaces. In this paper the study of this relationship in the ñire forests (Nothofagus antarctica) of the province of Tierra del Fuego (Argentina) is approached. Using trap cameras to monitor such interaction offers the opportunity to quickly collect large amounts of data. However, to take advantage of its potential, a large investment in trained personnel to analyze and filter the images of interest is required. The present work seeks to establish a path to significantly reduce this obstacle using the advances of machine and deep learning in the recognition of objects from images.XVII Workshop Computación Gráfica, Imágenes y Visualización.Red de Universidades con Carreras en Informática2019-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf438-445http://sedici.unlp.edu.ar/handle/10915/91024enginfo:eu-repo/semantics/altIdentifier/isbn/978-987-688-377-1info:eu-repo/semantics/reference/hdl/10915/90359info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:19:03Zoai:sedici.unlp.edu.ar:10915/91024Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:19:03.444SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Streamlining the study of the Tierra del Fuego forest through the use of deep learning
title Streamlining the study of the Tierra del Fuego forest through the use of deep learning
spellingShingle Streamlining the study of the Tierra del Fuego forest through the use of deep learning
Viera, Leonel
Ciencias Informáticas
Machine learning
Deep learning
Computer vision
Trap cameras
Forests
Image recognition
Ñire
Antarctic nothofagus
title_short Streamlining the study of the Tierra del Fuego forest through the use of deep learning
title_full Streamlining the study of the Tierra del Fuego forest through the use of deep learning
title_fullStr Streamlining the study of the Tierra del Fuego forest through the use of deep learning
title_full_unstemmed Streamlining the study of the Tierra del Fuego forest through the use of deep learning
title_sort Streamlining the study of the Tierra del Fuego forest through the use of deep learning
dc.creator.none.fl_str_mv Viera, Leonel
González, Federico
Soler, Rosina
Romano, Lucas
Feierherd, Guillermo Eugenio
author Viera, Leonel
author_facet Viera, Leonel
González, Federico
Soler, Rosina
Romano, Lucas
Feierherd, Guillermo Eugenio
author_role author
author2 González, Federico
Soler, Rosina
Romano, Lucas
Feierherd, Guillermo Eugenio
author2_role author
author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Machine learning
Deep learning
Computer vision
Trap cameras
Forests
Image recognition
Ñire
Antarctic nothofagus
topic Ciencias Informáticas
Machine learning
Deep learning
Computer vision
Trap cameras
Forests
Image recognition
Ñire
Antarctic nothofagus
dc.description.none.fl_txt_mv Understanding plant-herbivorous relationships allows to optimize the way to manage and protect natural spaces. In this paper the study of this relationship in the ñire forests (Nothofagus antarctica) of the province of Tierra del Fuego (Argentina) is approached. Using trap cameras to monitor such interaction offers the opportunity to quickly collect large amounts of data. However, to take advantage of its potential, a large investment in trained personnel to analyze and filter the images of interest is required. The present work seeks to establish a path to significantly reduce this obstacle using the advances of machine and deep learning in the recognition of objects from images.
XVII Workshop Computación Gráfica, Imágenes y Visualización.
Red de Universidades con Carreras en Informática
description Understanding plant-herbivorous relationships allows to optimize the way to manage and protect natural spaces. In this paper the study of this relationship in the ñire forests (Nothofagus antarctica) of the province of Tierra del Fuego (Argentina) is approached. Using trap cameras to monitor such interaction offers the opportunity to quickly collect large amounts of data. However, to take advantage of its potential, a large investment in trained personnel to analyze and filter the images of interest is required. The present work seeks to establish a path to significantly reduce this obstacle using the advances of machine and deep learning in the recognition of objects from images.
publishDate 2019
dc.date.none.fl_str_mv 2019-10
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/91024
url http://sedici.unlp.edu.ar/handle/10915/91024
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/isbn/978-987-688-377-1
info:eu-repo/semantics/reference/hdl/10915/90359
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
438-445
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
_version_ 1844616064342687744
score 13.070432

Publicaciones similares