Publicación:
Method to classify vegetation cover using satellite images and artificial intelligence

dc.contributor.authorSantivañez Isla, Luis Arturo
dc.contributor.authorAuccahuasi, Wilver
dc.contributor.authorRojas, Karin
dc.contributor.authorUrbano, Kitty
dc.contributor.authorCuzcano-Rivas, Abilio
dc.contributor.authorCarpio, Jorge Del
dc.contributor.authorFlores, Edward José
dc.contributor.authorFlores, Pedro
dc.contributor.authorBenites, Nicanor
dc.contributor.authorZamalloa, Leonidas
dc.date.accessioned2025-09-05T16:33:45Z
dc.description.abstractSpace technology is being used with greater emphasis in monitoring land cover, where the use of satellite images is used to analyze large areas of land, we can find optical satellite images that cover large areas of land, we present a methodology to be able to classify areas of vegetation cover present in the cadastre by means of satellite images, the classification is carried out by analyzing the chromatic characteristics that are extracted from the images. For which, two groups of images are created, corresponding to areas with the presence of vegetation and no vegetation. For the classification, the Matlab tool was used, from where a neural network was implemented to perform the classification, as well as a user interface for the use, manipulation and classification of the image, the results allow evaluating through the user interface of such that the neural network will be able to classify it. © 2023 Elsevier B.V., All rights reserved.
dc.identifier.doi10.1063/5.0125500
dc.identifier.isbn9780735451889
dc.identifier.scopus2-s2.0-85152798713
dc.identifier.urihttps://cris.uwiener.edu.pe/handle/001/375
dc.identifier.uuid06da60f8-f1f0-48cb-bafa-571162f7071a
dc.language.isoen
dc.publisherAmerican Institute of Physics Inc.
dc.relation.citationvolume2725
dc.relation.ispartofseriesAIP Conference Proceedings
dc.relation.issn0094243X
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.titleMethod to classify vegetation cover using satellite images and artificial intelligence
dc.typehttp://purl.org/coar/resource_type/c_f744
dspace.entity.typePublication

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