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dc.contributor.advisorNúñez González, José David
dc.contributor.advisorChica Páez, José Antonio
dc.contributor.authorBatmunkh, Baterdene
dc.contributor.otherMáster Universitario en Ingeniería Computacional y Sistemas Inteligentes
dc.contributor.otherKonputazio Ingeniaritza eta Sistema Adimentsuak Unibertsitate Masterra
dc.date.accessioned2022-12-23T09:20:46Z
dc.date.available2022-12-23T09:20:46Z
dc.date.issued2022-12-23
dc.identifier.urihttp://hdl.handle.net/10810/58973
dc.description.abstractGeospatial data treatment is an important task since it is a big part of big data. Nowadays, geospatial data exploitation is lacking in terms of artificial intelligence. In this work, we focus on the usage of a machine learning models to exploit geospatial data. We will follow a complete workflow from the collection and first descriptive analysis of the data to the development and evaluation of the different machine learning algorithms. From download dataset we will predict if the download will lead to civil work, in other words, it is a classification problem. We conclude that combining machine learning and geospatial data we can get a lot out of it.es_ES
dc.language.isoenges_ES
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectgeospatial dataes_ES
dc.subjectgeoreferenced infrastructure
dc.subjectcivil work prediction
dc.titleDevelopment of artificial intelligence models for the enrichment and exploitation of geospatial data in the built environmentes_ES
dc.typeinfo:eu-repo/semantics/masterThesis
dc.date.updated2022-09-07T06:03:01Z
dc.language.rfc3066es
dc.rights.holder© 2022, el autor
dc.contributor.degreeMáster Universitario en Ingeniería Computacional y Sistemas Inteligentes
dc.contributor.degreeKonputazio Ingeniaritza eta Sistema Adimentsuak Unibertsitate Masterra
dc.identifier.gaurregister126605-835109-11es_ES
dc.identifier.gaurassign127948-835109es_ES


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