Automatic generation of GIS vector Layers from orthomosaics using deep learning

ilustraciones, diagramas, mapas, tablas

Autores:: Ballesteros Parra, John Robert

Tipo de recurso:: Doctoral thesis

Fecha de publicación:: 2022

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: eng

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network_acronym_str	UNACIONAL2
network_name_str	Universidad Nacional de Colombia
repository_id_str
dc.title.eng.fl_str_mv	Automatic generation of GIS vector Layers from orthomosaics using deep learning
dc.title.translated.spa.fl_str_mv	Generación automatica de capas vectoriales SIG de ortomosaicos usando aprendizaje profundo
title	Automatic generation of GIS vector Layers from orthomosaics using deep learning
spellingShingle	Automatic generation of GIS vector Layers from orthomosaics using deep learning 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores Análisis vectorial Vector analysis Campos vectoriales Vector fields GIS Vectorization GAN Semantic Segmentation Orthomosaics Deep Learning Image Translation Image Caption Vectorización Redes Antagónicas Segmentación Semántica Ortomosaicos Aprendizaje Profundo Traducción de Imagen
title_short	Automatic generation of GIS vector Layers from orthomosaics using deep learning
title_full	Automatic generation of GIS vector Layers from orthomosaics using deep learning
title_fullStr	Automatic generation of GIS vector Layers from orthomosaics using deep learning
title_full_unstemmed	Automatic generation of GIS vector Layers from orthomosaics using deep learning
title_sort	Automatic generation of GIS vector Layers from orthomosaics using deep learning
dc.creator.fl_str_mv	Ballesteros Parra, John Robert
dc.contributor.advisor.none.fl_str_mv	Sanchez Torres, German Branch Bedoya, John Willian
dc.contributor.author.none.fl_str_mv	Ballesteros Parra, John Robert
dc.contributor.researchgroup.spa.fl_str_mv	Gidia: Grupo de Investigación y Desarrollo en Inteligencia Artificial
dc.subject.ddc.spa.fl_str_mv	000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores
topic	000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores Análisis vectorial Vector analysis Campos vectoriales Vector fields GIS Vectorization GAN Semantic Segmentation Orthomosaics Deep Learning Image Translation Image Caption Vectorización Redes Antagónicas Segmentación Semántica Ortomosaicos Aprendizaje Profundo Traducción de Imagen
dc.subject.lemb.none.fl_str_mv	Análisis vectorial Vector analysis Campos vectoriales Vector fields
dc.subject.proposal.eng.fl_str_mv	GIS Vectorization GAN Semantic Segmentation Orthomosaics Deep Learning Image Translation Image Caption
dc.subject.proposal.spa.fl_str_mv	Vectorización Redes Antagónicas Segmentación Semántica Ortomosaicos Aprendizaje Profundo Traducción de Imagen
description	ilustraciones, diagramas, mapas, tablas
publishDate	2022
dc.date.accessioned.none.fl_str_mv	2022-10-28T15:36:56Z
dc.date.available.none.fl_str_mv	2022-10-28T15:36:56Z
dc.date.issued.none.fl_str_mv	2022-10-12
dc.type.spa.fl_str_mv	Trabajo de grado - Doctorado
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/doctoralThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.coar.spa.fl_str_mv	http://purl.org/coar/resource_type/c_db06
dc.type.content.spa.fl_str_mv	Text
dc.type.redcol.spa.fl_str_mv	http://purl.org/redcol/resource_type/TD
format	http://purl.org/coar/resource_type/c_db06
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/82529
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/82529 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	eng
language	eng
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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Medellín - Minas - Doctorado en Ingeniería - Sistemas
dc.publisher.department.spa.fl_str_mv	Departamento de la Computación y la Decisión
dc.publisher.faculty.spa.fl_str_mv	Facultad de Minas
dc.publisher.place.spa.fl_str_mv	Medellín, Colombia
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Medellín
institution	Universidad Nacional de Colombia
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spelling	Reconocimiento 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Sanchez Torres, German7fe68c6418b4ba7cb12bdb0c00b9f23aBranch Bedoya, John Willian8373bc4285cc9e2e59e8f540f737e1db600Ballesteros Parra, John Robert8b10cddb5010474c877da0b0b9ef76faGidia: Grupo de Investigación y Desarrollo en Inteligencia Artificial2022-10-28T15:36:56Z2022-10-28T15:36:56Z2022-10-12https://repositorio.unal.edu.co/handle/unal/82529Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramas, mapas, tablasThis thesis presents a three methods pipeline for extraction of point, line, and polygon vector objects from orthomosaics using a deep generative model as an alternative to the default semantic segmentation approach. The first method consists of two workflows, the vector ground truth is acquired by manual digitalization of certain objects or from Open Street Maps. Raster layers input are spectral and geometrically augmented, both inputs are then tessellated and paired into image-masks that pass through an imbalance checking step. Balanced dataset is then random split into a final dataset. Conditional and unpaired generative models are compared and pix2pix is chosen by its better results on image to mask translation. Results of the chosen model on different datasets and configurations are reported on the mIoU metric. A batch size of 10 and datasets of 1000 image-masks pairs of 512x512 pixels, with overlapping augmentation showed the best quantitative results. Height of objects from the DSM, and VARI index contribute to decrease variance of discriminator and generator losses. Producing synthetic data is the horsepower of generative models, so a double image to mask translation is used to improve resultant masks in terms of continuity and uniform width. Double image to mask translation model is trained with a dataset of equal size masks of 1 meter called primitive masks, that are obtained by a buffer distance parameter. This cleaning procedure showed to improve resultant masks, that are then converted to vector and measured by quantity, length, or area against vector ground truth, using a proposed metric for map creation called “The average geometry similarity (AGS)”.Esta tesis presenta una metodología basada en tres métodos para la extracción de puntos, lineas, y polígonos de objetos vectoriales presentes en ortomosaicos usando un modelo generativo basado en aprendizaje profundo como una alternativa al enfoque de segmentación semántica usado por defecto. El primer método consiste en dos líneas de trabajo, las capas vector de entrenamiento son adquiridas bien sea por digitalización manual de los objetos de interés o directamente desde Open Street Maps (OSM). Las capas raster de entrada son aumentadas spectral y geométricamente, teseladas y emparejadas en pares imagen-mascara que se chequean ante el imbalance. El conjunto de datos balanceado es luego partido al azar para obtener el conjunto final. Los modelos generativos, condicionales y no emparejados son comparados y el mejor es escogido para realizar las traducciones entre imagen y mascara. Los resultados de la comparación y los obtenidos por el mejor modelo sobre diferentes conjuntos de datos, y su configuración son reportados usando la metrica mIoU. Un lote de tamaño diez para un conjunto de 1000 image-mascaras de 512x512 pixeles, con augmentación por solapamiento mostró los mejores resultados cuantitativos. La altura de los objetos obtenida del DSM, y el índice VARI contribuyen a disminuir la varianza del discriminador y del generador. La producción de datos sintéticos es el caballo de batalla de los modelos generativos, así que una doble traducción de imagen a mascara (DCIT) es empleada para mejorar las mascaras resultantes en términos de su continuidad y uniformidad. Un modelo para realizar DCIT es entrenado con un conjunto de datos de igual tamaño de mascara de 1 metro llamado mascaras primitivas, que son obtenidas usando una distancia buffer como parametro. Este procedimiento de limpieza mostró que mejora las mascaras resultantes, que son luego convertidas a vector y medidas en cantidad, distancia, o area vs la realidad vectorial, usando una métrica propuesta para la creación de mapas llamada “Similaridad geomética promedia (AGS)" (Texto tomado de la fuente)DoctoradoDoctor en IngenieríaInteligencia Artificial y MapasÁrea Curricular de Ingeniería de Sistemas e Informática138 páginasapplication/pdfengUniversidad Nacional de ColombiaMedellín - Minas - Doctorado en Ingeniería - SistemasDepartamento de la Computación y la DecisiónFacultad de MinasMedellín, ColombiaUniversidad Nacional de Colombia - Sede Medellín000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadoresAnálisis vectorialVector analysisCampos vectorialesVector fieldsGISVectorizationGANSemantic SegmentationOrthomosaicsDeep LearningImage TranslationImage CaptionVectorizaciónRedes AntagónicasSegmentación SemánticaOrtomosaicosAprendizaje ProfundoTraducción de ImagenAutomatic generation of GIS vector Layers from orthomosaics using deep learningGeneración automatica de capas vectoriales SIG de ortomosaicos usando aprendizaje profundoTrabajo de grado - Doctoradoinfo:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_db06Texthttp://purl.org/redcol/resource_type/TDAbdollahi, A., Pradhan, B., & Alamri, A. 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Unpaired Image-to-Image Translation Using Cycle Consistent Adversarial Networks. 2017 IEEE International Conference on Computer Vision (ICCV), 2242–2251. https://doi.org/10.1109/ICCV.2017.244EstudiantesInvestigadoresMaestrosLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/82529/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL98562187.2022.pdf98562187.2022.pdfTesis de Doctorado en Ingeniería - Sistemasapplication/pdf9568350https://repositorio.unal.edu.co/bitstream/unal/82529/2/98562187.2022.pdf9eb2c2f602243778e1909bae6075e3b8MD52THUMBNAIL98562187.2022.pdf.jpg98562187.2022.pdf.jpgGenerated Thumbnailimage/jpeg3974https://repositorio.unal.edu.co/bitstream/unal/82529/3/98562187.2022.pdf.jpgfb25a8e34423542d39be64cc59f80c53MD53unal/82529oai:repositorio.unal.edu.co:unal/825292024-08-12 02:00:20.742Repositorio Institucional Universidad Nacional de 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