AI turns to satellite imagery to enrich GPS maps: Page 3 of 3

January 27, 2020 //By Julien Happich
digital maps
A team of researchers at MIT and Qatar Computing Research Institute (QCRI) has developed a machine-learning model that leverages satellite imagery to tag road features in digital maps used for GPS navigation.

AI model RoadTagger uses satellite imagery to automatically
tag road features in digital maps. Image: Google Maps/MIT News

RoadTagger was first trained using a real-world map dataset, called OpenStreetMap, which lets users edit and curate digital maps around the globe. From that dataset, they collected confirmed road attributes from 688 square kilometers of maps of 20 U.S. cities — including Boston, Chicago, Washington, and Seattle. Then, they gathered the corresponding satellite images from a Google Maps dataset.

The researchers hope to use RoadTagger to help humans rapidly validate and approve continuous modifications to infrastructure in datasets such as OpenStreetMap, where many maps don’t contain lane counts or other details. A specific area of interest is Thailand, Bastani says, where roads are constantly changing, but there are few if any updates in the dataset.

“Roads that were once labelled as dirt roads have been paved over so are better to drive on, and some intersections have been completely built over. There are changes every year, but digital maps are out of date,” he says. “We want to constantly update such road attributes based on the most recent imagery.”


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