The secret of Google Maps’ accuracy revealed

Greg Miller in www.wired.co.uk  9 Dec 2014 (TOH GISCafe)

The maps we use to navigate have come a long way in a short time. Since the ’90s we’ve gone from glove boxes stuffed with paper maps to floorboards littered with Mapquest printouts to mindlessly obeying Siri or her nameless Google counterpart.

The maps behind those voices are packed with far more data than most people realize. On a recent visit to Mountain View, I got a peek at how the Google Maps team assembles their maps and refines them with a combination of algorithms and meticulous manual labor — an effort they call Ground Truth. The project launched in 2008, but it was mostly kept under wraps until just a couple years ago. It continues to grow, now covering 51 countries, and algorithms are playing a bigger role in extracting information from satellite, aerial, and Street View imagery.
A few of the features that can be extracted algorithmically from Google Street View dataGoogle Maps
Street View, which launched in 2007, was conceived as a way to improve the user experience by letting people see what the area around their destination looked like, says Brian McClendon, Google Maps VP. “But we soon realized that one of the best ways to make maps is to have a photographic record of the streets of the world and refer back to those whenever there’s a correction,” McClendon said.

And as the data collected by Street View grew, the team saw that it was good for more than just spot-checking their data, says Manik Gupta, group product manager for Google Maps. Street View cars have now driven more than 7 million miles, including 99 percent of the public roads in the U.S. “It’s actually allowing us to algorithmically build up new data layers from information we’ve extracted,” Gupta said.
Invisible to ordinary users, information about turn restrictions are built into Google mapsGoogle Maps
Those algorithms borrow methods from computer vision and machine learning to extract features like street numbers painted on curbs, the names of businesses and other points of interest, speed limits and other traffic signs. “Stop signs are trivial, they’re made to stick out,” McClendon said. Turn restrictions — which directions you can turn at a given intersection — are a big deal for navigation, but they’re trickier to capture with algorithms. Sometimes the arrows that tell you which turns are legal are painted on the road, sometimes they’re overhead. They can be different colours and sizes. “Lane markers are harder because they’re not consistent, but we’re getting much smarter about that,” McClendon said.

Street signs are a big deal too. Drivers can follow the app’s verbal directions more easily if what they hear matches what they see. but sometimes the spelling or abbreviation used on street signs varies. “Matching what’s written on the signs is actually a hard and important problem,” McClendon said.

Other algorithms extract building footprints and heights from satellite and aerial imagery. The majority of buildings in the U.S. are now on Google Maps. For landmarks like Seattle’s Space Needle, computer vision techniques extract detailed 3D models (see below). Google has said that its recent acquisition of Skybox, the high-resolution satellite imagery company, at least initially, is to improve the accuracy of its maps.
Google uses computer vision techniques to extract 3D models of landmark buildings from satellite and aerial imageryGoogle Maps
Yet satellites and algorithms only get you so far. Google employs a small army of human operators (they won’t say exactly how many) to manually check and correct the maps using an in-house program called Atlas. Few people outside the company have seen it in use, but one of the most prolific operators on the map team, Nick Volmar, demonstrated the program during my visit. (There’s also a fascinating demo in this video from Google’s 2013 developers conference).  Click here to continue reading.

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