Machine-assisted map editing Article Swipe
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· 2018
· Open Access
·
· DOI: https://doi.org/10.1145/3274895.3274927
· OA: W2900464026
Mapping road networks today is labor-intensive. As a result, road maps have\npoor coverage outside urban centers in many countries. Systems to automatically\ninfer road network graphs from aerial imagery and GPS trajectories have been\nproposed to improve coverage of road maps. However, because of high error\nrates, these systems have not been adopted by mapping communities. We propose\nmachine-assisted map editing, where automatic map inference is integrated into\nexisting, human-centric map editing workflows. To realize this, we build\nMachine-Assisted iD (MAiD), where we extend the web-based OpenStreetMap editor,\niD, with machine-assistance functionality. We complement MAiD with a novel\napproach for inferring road topology from aerial imagery that combines the\nspeed of prior segmentation approaches with the accuracy of prior iterative\ngraph construction methods. We design MAiD to tackle the addition of major,\narterial roads in regions where existing maps have poor coverage, and the\nincremental improvement of coverage in regions where major roads are already\nmapped. We conduct two user studies and find that, when participants are given\na fixed time to map roads, they are able to add as much as 3.5x more roads with\nMAiD.\n
