  Mapping road networks today is labor-intensive. As a result, road maps have
poor coverage outside urban centers in many countries. Systems to automatically
infer road network graphs from aerial imagery and GPS trajectories have been
proposed to improve coverage of road maps. However, because of high error
rates, these systems have not been adopted by mapping communities. We propose
machine-assisted map editing, where automatic map inference is integrated into
existing, human-centric map editing workflows. To realize this, we build
Machine-Assisted iD (MAiD), where we extend the web-based OpenStreetMap editor,
iD, with machine-assistance functionality. We complement MAiD with a novel
approach for inferring road topology from aerial imagery that combines the
speed of prior segmentation approaches with the accuracy of prior iterative
graph construction methods. We design MAiD to tackle the addition of major,
arterial roads in regions where existing maps have poor coverage, and the
incremental improvement of coverage in regions where major roads are already
mapped. We conduct two user studies and find that, when participants are given
a fixed time to map roads, they are able to add as much as 3.5x more roads with
MAiD.
