MIT scientists have developed an artificial intelligence system that can automatically trace out roads from aerial images of a city.
Mapping roads for navigation apps can be tedious. Even after taking aerial images, companies like Google still have to spend many hours manually tracing out roads.
As a result, vast majority of the more than 20 million miles of roads across the globe have not yet been mapped.
Gaps in maps are a problem, particularly for systems being developed for self-driving cars. To address the issue, researchers have created RoadTracer, an automated method to build road maps that's 45 percent more accurate than existing approaches.
Using data from aerial images, the team says that RoadTracer is not just more accurate, but more cost-effective than current approaches.
"RoadTracer is well-suited to map areas of the world where maps are frequently out of date, which includes both places with lower population and areas where there's frequent construction," said Mohammad Alizadeh, professor at Massachusetts Institute of Technology in the US.
"For example, existing maps for remote areas like rural Thailand are missing many roads. RoadTracer could help make them more accurate," said Alizadeh.
In tests looking at aerial images of New York City, RoadTracer could correctly map 44 per cent of its road junctions, which is more than twice as effective as traditional approaches based on image segmentation that could map only 19 per cent.
Current efforts to automate maps involve training neural networks to look at aerial images and identify individual pixels as either "road" or "not road."
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