# Machine-Assisted Map Editing

## TL;DR

This paper introduces Machine-Assisted iD (MAiD), a tool that integrates automatic road inference into human map editing workflows, significantly increasing mapping efficiency especially in poorly covered regions.

## Contribution

The paper presents MAiD, a novel web-based tool that combines fast aerial imagery analysis with human editing to improve road map coverage efficiently.

## Key findings

- Participants added up to 3.5 times more roads with MAiD.
- MAiD effectively enhances coverage of major roads in underserved areas.
- The approach balances speed and accuracy in road inference.

## Abstract

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.