Refining inferred road maps using GANs

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.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Park, Edward(Edward S.),M. Eng.Massachusetts Institute of Technology.

Advisor dc:contributor.advisor

• Mohammad Alizadeh.

• Electrical Engineering and Computer Science.

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/123037

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/123037