Containers in Montevideo: a Multi source Image Dataset

Autores
Laguna, Rodrigo; Moncecchi, Guillermo
Año de publicación
2021
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
This work presents Clean-Dirty Containers in Montevideo (CDCM), a novel dataset for detection and classi cation of residue con-tainers. Images were collected from several sources, including Google Street View, Social Networks and smarthpone taken photos. The dataset is publicly available under a Creative Commons License.
Sociedad Argentina de Informática e Investigación Operativa
Materia
Ciencias Informáticas
Multi source Dataset
Smart Cities
Open Data
Image Recognition
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:10915/140247

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network_name_str SEDICI (UNLP)
spelling Containers in Montevideo: a Multi source Image DatasetLaguna, RodrigoMoncecchi, GuillermoCiencias InformáticasMulti source DatasetSmart CitiesOpen DataImage RecognitionThis work presents Clean-Dirty Containers in Montevideo (CDCM), a novel dataset for detection and classi cation of residue con-tainers. Images were collected from several sources, including Google Street View, Social Networks and smarthpone taken photos. The dataset is publicly available under a Creative Commons License.Sociedad Argentina de Informática e Investigación Operativa2021-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf91-102http://sedici.unlp.edu.ar/handle/10915/140247enginfo:eu-repo/semantics/altIdentifier/url/http://50jaiio.sadio.org.ar/pdfs/asai/ASAI-11.pdfinfo:eu-repo/semantics/altIdentifier/issn/2451-7585info: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:UNLP2025-09-29T11:35:34Zoai:sedici.unlp.edu.ar:10915/140247Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:35:35.22SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Containers in Montevideo: a Multi source Image Dataset
title Containers in Montevideo: a Multi source Image Dataset
spellingShingle Containers in Montevideo: a Multi source Image Dataset
Laguna, Rodrigo
Ciencias Informáticas
Multi source Dataset
Smart Cities
Open Data
Image Recognition
title_short Containers in Montevideo: a Multi source Image Dataset
title_full Containers in Montevideo: a Multi source Image Dataset
title_fullStr Containers in Montevideo: a Multi source Image Dataset
title_full_unstemmed Containers in Montevideo: a Multi source Image Dataset
title_sort Containers in Montevideo: a Multi source Image Dataset
dc.creator.none.fl_str_mv Laguna, Rodrigo
Moncecchi, Guillermo
author Laguna, Rodrigo
author_facet Laguna, Rodrigo
Moncecchi, Guillermo
author_role author
author2 Moncecchi, Guillermo
author2_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
Multi source Dataset
Smart Cities
Open Data
Image Recognition
topic Ciencias Informáticas
Multi source Dataset
Smart Cities
Open Data
Image Recognition
dc.description.none.fl_txt_mv This work presents Clean-Dirty Containers in Montevideo (CDCM), a novel dataset for detection and classi cation of residue con-tainers. Images were collected from several sources, including Google Street View, Social Networks and smarthpone taken photos. The dataset is publicly available under a Creative Commons License.
Sociedad Argentina de Informática e Investigación Operativa
description This work presents Clean-Dirty Containers in Montevideo (CDCM), a novel dataset for detection and classi cation of residue con-tainers. Images were collected from several sources, including Google Street View, Social Networks and smarthpone taken photos. The dataset is publicly available under a Creative Commons License.
publishDate 2021
dc.date.none.fl_str_mv 2021-10
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Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
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