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

dc.rights.licenseReconocimiento 4.0 Internacional
dc.contributor.advisorNiño Vásquez, Luís Fernando
dc.contributor.authorMontero Leguizamón, Aníbal
dc.date.accessioned2021-10-04T14:59:48Z
dc.date.available2021-10-04T14:59:48Z
dc.date.issued2021-09-29
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/80363
dc.descriptionilustraciones, fotografías, mapas, tablas
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)
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.
dc.format.extentXVI, 46 páginas
dc.format.mimetypeapplication/pdf
dc.language.isospa
dc.publisherUniversidad Nacional de Colombia
dc.rightsDerechos reservados al autor
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject.ddc000 - Ciencias de la computación, información y obras generales
dc.titleAplicación de una red neuronal convolucional para la predicción de mallas de población en el área metropolitana de Bogotá
dc.typeTrabajo de grado - Maestría
dc.type.driverinfo:eu-repo/semantics/masterThesis
dc.type.versioninfo:eu-repo/semantics/acceptedVersion
dc.publisher.programBogotá - Ingeniería - Maestría en Ingeniería - Ingeniería de Sistemas y Computación
dc.contributor.researchgroupLABORATORIO DE INVESTIGACIÓN EN SISTEMAS INTELIGENTES - LISI
dc.coverage.cityBogotá
dc.coverage.countryColombia
dc.description.degreelevelMaestría
dc.description.degreenameMagíster en Ingeniería - Ingeniería de Sistemas y Computación
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.
dc.description.researchareaSistemas Inteligentes
dc.identifier.instnameUniversidad Nacional de Colombia
dc.identifier.reponameRepositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourlhttps://repositorio.unal.edu.co/
dc.publisher.departmentDepartamento de Ingeniería de Sistemas e Industrial
dc.publisher.facultyFacultad de Ingeniería
dc.publisher.branchUniversidad Nacional de Colombia - Sede Bogotá
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dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.subject.lembRedes neuronales
dc.subject.lembNeural networks
dc.subject.proposalMalla de Población
dc.subject.proposalSensores Remotos
dc.subject.proposalAprendizaje Profundo
dc.subject.proposalRedes Neuronales Convolucionales
dc.subject.proposalPopulation grid
dc.subject.proposalRemote Sensing
dc.subject.proposalDeep Learning
dc.subject.proposalConvolutional Neural Networks
dc.subject.unescoProyección demográfica
dc.subject.unescoPopulation projections
dc.title.translatedPredicting a population grid in the Bogotá metropolitan area, based on convolutional neural networks
dc.type.coarhttp://purl.org/coar/resource_type/c_bdcc
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.contentText
dc.type.redcolhttp://purl.org/redcol/resource_type/TM
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dcterms.audience.professionaldevelopmentEstudiantes
dcterms.audience.professionaldevelopmentInvestigadores
dcterms.audience.professionaldevelopmentMaestros


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Reconocimiento 4.0 InternacionalEsta obra está bajo licencia internacional Creative Commons Reconocimiento-NoComercial 4.0.Este documento ha sido depositado por parte de el(los) autor(es) bajo la siguiente constancia de depósito