Urban Pedestrian Routes’ Accessibility Assessment Using Geographic Information System Processing and Deep Learning-Based Object Detection

dc.contributor.author	Martínez Chao, T. E.
dc.contributor.author	Menéndez Díaz, Agustín
dc.contributor.author	García Cortés, Silverio
dc.contributor.author	D’Agostino, P.
dc.date.accessioned	2024-11-07T07:25:49Z
dc.date.available	2024-11-07T07:25:49Z
dc.date.issued	2024
dc.identifier.citation	Sensors, 24(11), (2024); doi:10.3390/s24113667
dc.identifier.issn	1424-8220
dc.identifier.uri	https://hdl.handle.net/10651/75582
dc.description.sponsorship	he authors would like to acknowledge the Banco de Santander and the University of Oviedo for their granting of financial aid for mobility of excellence for teachers and researchers granted on 1 May 2024; (BOPA. 132, 11-VII-2023) https://www.uniovi.es/documents/39158/360878 8/CONVOCATORIA+BOPA+11-VII-2023.pdf
dc.language.iso	eng
dc.relation.ispartof	Sensors
dc.rights	© 2024 by the authors.
dc.rights	CC Reconocimiento 4.0 Internacional
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/
dc.source	Scopus
dc.source.uri	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195964923&doi=10.3390%2fs24113667&partnerID=40&md5=2c5cbff328ae46af4c16f2a419ab8041
dc.title	Urban Pedestrian Routes’ Accessibility Assessment Using Geographic Information System Processing and Deep Learning-Based Object Detection
dc.type	journal article
dc.identifier.doi	10.3390/s24113667
dc.relation.publisherversion	http://dx.doi.org/10.3390/s24113667
dc.rights.accessRights	open access

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