2026-09-19T07:29:57Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1230372026-06-06T00:55:13Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Mohammad Alizadeh.
Park, Edward(Edward S.),M. Eng.Massachusetts Institute of Technology.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2019-11-22T00:03:39Z
2019-11-22T00:03:39Z
2019
2019
https://hdl.handle.net/1721.1/123037
1127827706
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 63-64).
Mapping 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.
by Edward Park.
M.Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
64 pages
eng
Massachusetts Institute of Technology
MIT 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.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Refining inferred road maps using GANs
Refining inferred road maps using Generative Adversarial Networks
Thesis
application/pdf
2019-11-22T00:03:37Z
Publication
Master
EECS
unknown
unknown
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<Abstract>Mapping 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.</Abstract>
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