Aplicación de una red neuronal convolucional para la predicción de mallas de población en el área metropolitana de Bogotá

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dc.contributor.advisorNiño Vásquez, Luís Fernando
dc.contributor.authorMontero Leguizamón, Aníbal
dc.contributor.researchgroupLABORATORIO DE INVESTIGACIÓN EN SISTEMAS INTELIGENTES - LISIspa
dc.coverage.cityBogotá
dc.coverage.countryColombia
dc.date.accessioned2021-10-04T14:59:48Z
dc.date.available2021-10-04T14:59:48Z
dc.date.issued2021-09-29
dc.descriptionilustraciones, fotografías, mapas, tablasspa
dc.description.abstractSaber cuántas personas viven en un área determinada y saber en dónde habitan específicamente, son preguntas tradicionalmente abordadas desde la Demografía. El presente trabajo plantea la alternativa de utilizar imágenes satelitales para predecir el número de habitantes a partir de mallas de población. Se extrajo un conjunto de imágenes de Landsat 8, a partir de un diseño muestral proporcional al tamaño (PPS) aplicado sobre una malla de población censal del 2018 en Colombia. Se entrenó la arquitectura LeNet-5 modificada para realizar regresión sobre el número de habitantes por celda a partir del conjunto de imágenes obtenido. Se realizaron pruebas del modelado sobre una submuestra de la malla de población en Colombia y sobre la malla de población correspondiente a los municipios que componen el área metropolitana de Bogotá en 2018, arrojando MAEs de 947,8 y 1.181,9, respectivamente, igualando e incluso superando los resultados encontrados en el estado del arte. (Texto tomado de la fuente)spa
dc.description.abstractKnowing how many people live in an area and knowing where they live specifically are questions commonly approached through Demography. The present work proposes the using of satellite images to predict the number of inhabitants based on population grids as an alternative approach. A Landsat 8 images dataset was generated using a Probability Proportional to Size (PPS) sample extracted on a 2018 census population grid in Colombia. A LeNet-5 architecture was modified to predict the number of inhabitants per cell and trained with the previous image dataset obtained. The trained model was tested with a subsample of the population grid in Colombia and the population grid corresponding to the municipalities of the Bogotá metropolitan area in 2018. The model reached MAEs of 947.8 and 1181.9, respectively. These results equal and even exceed the performance found in the state of the art.eng
dc.description.degreelevelMaestríaspa
dc.description.degreenameMagíster en Ingeniería - Ingeniería de Sistemas y Computaciónspa
dc.description.methodsSe abordó una estrategia de investigación cuantitativa. Tal y como se relacionan los conceptos mencionados en el título y el objetivo general de la investigación, y en cuanto a la definición operacional de las variables implicadas en la investigación, se modeló la malla de población en el área metropolitana de Bogotá a partir de las características detectadas en las imágenes de sensores remotos a través de CNN. se adoptó un tipo de investigación no experimental y con significación temporal de tipo transversal.spa
dc.description.researchareaSistemas Inteligentesspa
dc.format.extentXVI, 46 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.identifier.instnameUniversidad Nacional de Colombiaspa
dc.identifier.reponameRepositorio Institucional Universidad Nacional de Colombiaspa
dc.identifier.repourlhttps://repositorio.unal.edu.co/spa
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/80363
dc.language.isospaspa
dc.publisherUniversidad Nacional de Colombiaspa
dc.publisher.branchUniversidad Nacional de Colombia - Sede Bogotáspa
dc.publisher.departmentDepartamento de Ingeniería de Sistemas e Industrialspa
dc.publisher.facultyFacultad de Ingenieríaspa
dc.publisher.programBogotá - Ingeniería - Maestría en Ingeniería - Ingeniería de Sistemas y Computaciónspa
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dc.rightsDerechos reservados al autorspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.licenseReconocimiento 4.0 Internacionalspa
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/spa
dc.subject.ddc000 - Ciencias de la computación, información y obras generalesspa
dc.subject.lembRedes neuronalesspa
dc.subject.lembNeural networkseng
dc.subject.proposalMalla de Poblaciónspa
dc.subject.proposalSensores Remotosspa
dc.subject.proposalAprendizaje Profundospa
dc.subject.proposalRedes Neuronales Convolucionalesspa
dc.subject.proposalPopulation grideng
dc.subject.proposalRemote Sensingeng
dc.subject.proposalDeep Learningeng
dc.subject.proposalConvolutional Neural Networkseng
dc.subject.unescoProyección demográficaspa
dc.subject.unescoPopulation projectionseng
dc.titleAplicación de una red neuronal convolucional para la predicción de mallas de población en el área metropolitana de Bogotáspa
dc.title.translatedPredicting a population grid in the Bogotá metropolitan area, based on convolutional neural networkseng
dc.typeTrabajo de grado - Maestríaspa
dc.type.coarhttp://purl.org/coar/resource_type/c_bdccspa
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/masterThesisspa
dc.type.redcolhttp://purl.org/redcol/resource_type/TMspa
dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
dcterms.audience.professionaldevelopmentEstudiantesspa
dcterms.audience.professionaldevelopmentInvestigadoresspa
dcterms.audience.professionaldevelopmentMaestrosspa
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2spa

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