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dc.contributor.advisorMohammad Alizadeh.en_US
dc.contributor.authorPark, Edward(Edward S.),M. Eng.Massachusetts Institute of Technology.en_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2019-11-22T00:03:39Z
dc.date.available2019-11-22T00:03:39Z
dc.date.copyright2019en_US
dc.date.issued2019en_US
dc.identifier.urihttps://hdl.handle.net/1721.1/123037
dc.descriptionThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.en_US
dc.descriptionThesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019en_US
dc.descriptionCataloged from student-submitted PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 63-64).en_US
dc.description.abstractMapping road networks is both expensive and labor-intensive. A variety of automated mapping approaches have been proposed in recent years, but these schemes often produce maps that are messy, error-prone, or visually unappealing. To fix this, we train a conditional Wasserstein GAN to refine the inferred road map and improve its realism. We show that adding a truth padding stage between the discriminator and generator vastly improves tile consistency, and we introduce a postprocessing pipeline to further clean the graph. To evaluate these results, we focus on a state-of-the-art map inference method known as RoadTracer, published in 2018 by MIT and QCRI. We compare our refinement approach with the original RoadTracer input and easily see qualitative improvements with similar topology.en_US
dc.description.statementofresponsibilityby Edward Park.en_US
dc.format.extent64 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleRefining inferred road maps using GANsen_US
dc.title.alternativeRefining inferred road maps using Generative Adversarial Networksen_US
dc.typeThesisen_US
dc.description.degreeM. Eng.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.identifier.oclc1127827706en_US
dc.description.collectionM.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Scienceen_US
dspace.imported2019-11-22T00:03:37Zen_US
mit.thesis.degreeMasteren_US
mit.thesis.departmentEECSen_US


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