RUJA: Repositorio Institucional de Producción Científica

 

Deep learning methods applied to digital elevation models: state of the art

dc.contributor.authorRuiz-Lendínez, Juan José
dc.contributor.authorAriza-López, Francisco Javier
dc.contributor.authorReinoso, Juan Francisco
dc.contributor.authorUreña, Manuel Antonio
dc.contributor.authorQuesada-Real, Francisco José
dc.date.accessioned2025-01-22T08:59:22Z
dc.date.available2025-01-22T08:59:22Z
dc.date.issued2023-09-06
dc.description.abstractDeep Learning (DL) has a wide variety of applications in various thematic domains, including spatial information. Although with limitations, it is also starting to be considered in operations related to Digital Elevation Models (DEMs). This study aims to review the methods of DL applied in the field of altimetric spatial information in general, and DEMs in particular. Void Filling (VF), Super-Resolution (SR), landform classification and hydrography extraction are just some of the operations where traditional methods are being replaced by DL methods. Our review concludes that although these methods have great potential, there are aspects that need to be improved. More appropriate terrain information or algorithm parameterisation are some of the challenges that this methodology still needs to face.es_ES
dc.identifier.citationRuiz-Lendínez, J. J., Ariza-López, F. J., Reinoso-Gordo, J. F., Ureña-Cámara, M. A., & Quesada-Real, F. J. (2023). Deep learning methods applied to digital elevation models: state of the art. Geocarto International, 38(1). https://doi.org/10.1080/10106049.2023.2252389es_ES
dc.identifier.issn1010-6049es_ES
dc.identifier.other10.1080/10106049.2023.2252389es_ES
dc.identifier.urihttps://www.tandfonline.com/doi/full/10.1080/10106049.2023.2252389es_ES
dc.identifier.urihttps://hdl.handle.net/10953/4247
dc.language.isoenges_ES
dc.publisherTaylor&Francises_ES
dc.relation.ispartofGeocarto Internationales_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectDeep learninges_ES
dc.subjectDEMses_ES
dc.subjectVoid fillinges_ES
dc.subjectSuper-resolutiones_ES
dc.subjectLandform classificationes_ES
dc.titleDeep learning methods applied to digital elevation models: state of the artes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.type.versioninfo:eu-repo/semantics/publishedVersiones_ES

Archivos

Bloque original

Mostrando 1 - 1 de 1
Cargando...
Miniatura
Nombre:
2023-Geocarto International_Deep Learning Methods applied to DTMs_Q2.pdf
Tamaño:
2.35 MB
Formato:
Adobe Portable Document Format
Descripción:

Bloque de licencias

Mostrando 1 - 1 de 1
No hay miniatura disponible
Nombre:
license.txt
Tamaño:
1.98 KB
Formato:
Item-specific license agreed upon to submission
Descripción:

Colecciones