Creating and maintaining maps is both expensive and labor-intensive. As a result, maps often have poor coverage or contain outdated information, particularly of locations outside urban areas. Additionally, street map metadata, such as the number of lanes, speed limits, turn restrictions, and the positions of crosswalks and parking spaces, is often of poor quality or completely missing. This metadata is crucial for navigation applications and autonomous vehicles.

The goal is to leverage GPS trajectories, satellite and aerial imagery, drone imagery, and other data sources to improve the accuracy and coverage of maps, and to reduce the delay between physical road network changes and updates to the map.

However, despite over a decade of research in automatic map inference systems that automatically construct road maps, these systems have not gained traction in OpenStreetMap and other mapping communities. High error rates (5% to 10% even in state-of-the-art systems) make straightforward integration of automatically inferred roads into the map impractical.

We built Machine-Assisted iD (MAiD), where we extended the web-based OpenStreetMap editor, iD, with machine-assistance functionality. By tackling 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, MAiD substantially improves mapping productivity. You can see demos of the product below.

At its core, MAiD replaces manual tracing of roads with human validation of automatically inferred road segments. We designed MAiD with a holistic view of the map-editing process, focusing on the parts of the workflow that can benefit substantially from machine assistance. We complement MAiD with a novel approach for inferring road topology from aerial imaging that combines the speed of prior segmentation approaches with the accuracy of prior iterative graph construction methods.