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Automatic generation of GIS vector Layers from orthomosaics using deep learning
dc.rights.license | Reconocimiento 4.0 Internacional |
dc.contributor.advisor | Sanchez Torres, German |
dc.contributor.advisor | Branch Bedoya, John Willian |
dc.contributor.author | Ballesteros Parra, John Robert |
dc.date.accessioned | 2022-10-28T15:36:56Z |
dc.date.available | 2022-10-28T15:36:56Z |
dc.date.issued | 2022-10-12 |
dc.identifier.uri | https://repositorio.unal.edu.co/handle/unal/82529 |
dc.description | ilustraciones, diagramas, mapas, tablas |
dc.description.abstract | This 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)”. |
dc.description.abstract | 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) |
dc.format.extent | 138 páginas |
dc.format.mimetype | application/pdf |
dc.language.iso | eng |
dc.publisher | Universidad Nacional de Colombia |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ |
dc.subject.ddc | 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores |
dc.title | Automatic generation of GIS vector Layers from orthomosaics using deep learning |
dc.type | Trabajo de grado - Doctorado |
dc.type.driver | info:eu-repo/semantics/doctoralThesis |
dc.type.version | info:eu-repo/semantics/acceptedVersion |
dc.publisher.program | Medellín - Minas - Doctorado en Ingeniería - Sistemas |
dc.contributor.researchgroup | Gidia: Grupo de Investigación y Desarrollo en Inteligencia Artificial |
dc.description.degreelevel | Doctorado |
dc.description.degreename | Doctor en Ingeniería |
dc.description.researcharea | Inteligencia Artificial y Mapas |
dc.identifier.instname | Universidad Nacional de Colombia |
dc.identifier.reponame | Repositorio Institucional Universidad Nacional de Colombia |
dc.identifier.repourl | https://repositorio.unal.edu.co/ |
dc.publisher.department | Departamento de la Computación y la Decisión |
dc.publisher.faculty | Facultad de Minas |
dc.publisher.place | Medellín, Colombia |
dc.publisher.branch | Universidad Nacional de Colombia - Sede Medellín |
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dc.rights.accessrights | info:eu-repo/semantics/openAccess |
dc.subject.lemb | Análisis vectorial |
dc.subject.lemb | Vector analysis |
dc.subject.lemb | Campos vectoriales |
dc.subject.lemb | Vector fields |
dc.subject.proposal | GIS |
dc.subject.proposal | Vectorization |
dc.subject.proposal | GAN |
dc.subject.proposal | Semantic Segmentation |
dc.subject.proposal | Orthomosaics |
dc.subject.proposal | Deep Learning |
dc.subject.proposal | Image Translation |
dc.subject.proposal | Image Caption |
dc.subject.proposal | Vectorización |
dc.subject.proposal | Redes Antagónicas |
dc.subject.proposal | Segmentación Semántica |
dc.subject.proposal | Ortomosaicos |
dc.subject.proposal | Aprendizaje Profundo |
dc.subject.proposal | Traducción de Imagen |
dc.title.translated | Generación automatica de capas vectoriales SIG de ortomosaicos usando aprendizaje profundo |
dc.type.coar | http://purl.org/coar/resource_type/c_db06 |
dc.type.coarversion | http://purl.org/coar/version/c_ab4af688f83e57aa |
dc.type.content | Text |
dc.type.redcol | http://purl.org/redcol/resource_type/TD |
oaire.accessrights | http://purl.org/coar/access_right/c_abf2 |
dcterms.audience.professionaldevelopment | Estudiantes |
dcterms.audience.professionaldevelopment | Investigadores |
dcterms.audience.professionaldevelopment | Maestros |
dc.description.curriculararea | Área Curricular de Ingeniería de Sistemas e Informática |
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