Inferring and Improving Street Maps with Data-Driven Automation

arXiv · October 2, 2019 · Notable

Summary

Researchers at MIT and QCRI developed Mapster, a human-in-the-loop street map editing system. Mapster incorporates high-precision automatic map inference, data refinement, and machine-assisted map editing. Evaluation across forty cities using satellite imagery, GPS trajectories, and ground-truth data demonstrates Mapster's ability to make automation practical for map editing. Why it matters: This system could significantly improve the accuracy and completeness of street maps in rapidly developing urban areas across the Middle East.

Keywords

street maps · automation · satellite imagery · GPS trajectories · QCRI

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