Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/70722
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dc.contributor.authorGarcía-Pedrero, Ángelen_US
dc.contributor.authorLillo-Saavedra, Marioen_US
dc.contributor.authorRodríguez Esparragón, Dionisioen_US
dc.contributor.authorGonzalo Martin,Consueloen_US
dc.date.accessioned2020-03-06T13:22:01Z-
dc.date.available2020-03-06T13:22:01Z-
dc.date.issued2019en_US
dc.identifier.issn2169-3536en_US
dc.identifier.urihttp://hdl.handle.net/10553/70722-
dc.description.abstractAccurate and up-to-date information on the spatial and geographical characteristics of agricultural areas is an indispensable value for the various activities related to agriculture and research. Most agricultural studies and policies are carried out at the field level, for which precise boundaries are required. Today, high-resolution remote sensing images provide useful spatial information for plot delineation; however, manual processing is time-consuming and prone to human error. The objective of this paper is to explore the potential of deep learning (DL) approach, in particular a convolutional neural network (CNN) model, for the automatic outlining of agricultural plot boundaries from orthophotos over large areas with a heterogeneous landscape. Since DL approaches require a large amount of labeled data to learn, we have exploited the open data from the Land Parcel Identification System (LPIS) from the Chartered Community of Navarre, Spain. The boundaries of the agricultural plots obtained from our methodology were compared with those obtained using a state-of-the-art methodology known as gPb-UCM (global probability of boundary followed by ultrametric contour map) through an error measurement called the boundary displacement error index (BDE). In BDE terms, the results obtained by our method outperform those obtained from the gPb-UCM method. In this regard, CNN models trained with LPIS data are a useful and powerful tool that would reduce intensive manual labor in outlining agricultural plots.en_US
dc.languageengen_US
dc.relation.ispartofIEEE Accessen_US
dc.sourceIEEE Access [ISSN 2169-3536], v. 7, p. 158223-158236en_US
dc.subject250616 Teledetección (Geología)en_US
dc.subject.otherConvolutional neural networken_US
dc.subject.otherDeep learningen_US
dc.subject.otherEdge extractionen_US
dc.subject.otherLand parcel identification systemen_US
dc.subject.otherParcels delineationen_US
dc.titleDeep Learning for Automatic Outlining Agricultural Parcels: Exploiting the Land Parcel Identification Systemen_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/ACCESS.2019.2950371en_US
dc.investigacionCienciasen_US
dc.type2Artículoen_US
dc.utils.revisionen_US
dc.identifier.ulpgces
item.fulltextCon texto completo-
item.grantfulltextopen-
crisitem.author.deptIOCAG: Procesado de Imágenes y Teledetección-
crisitem.author.deptIU de Oceanografía y Cambio Global-
crisitem.author.deptDepartamento de Señales y Comunicaciones-
crisitem.author.orcid0000-0002-4542-2501-
crisitem.author.parentorgIU de Oceanografía y Cambio Global-
crisitem.author.fullNameRodríguez Esparragón, Dionisio-
crisitem.author.fullNameGonzalo Martin,Consuelo-
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