New types of mobility systems, geo-targeting of customers and the emergence of more delivery services is creating demand for mapping data that is richer and more intelligent. Mapillary, a Swedish startup that applies computer vision to a growing volume of crowdsourced street-level imagery to create maps that are constantly updated and richer in detail, is aiming to provide just that.
The company counts BMW, Toyota, Volkswagen, the World Bank and Lyft among its partners and investors.
“The expectations for people using maps is very different than it was 10 years ago,” says Mapillary CEO and cofounder Jan Erik Solem.“More and more companies need maps to be competitive, some even have their own internal mapping teams. The ridesharing map is different from the delivery map which is different from the scooter map. With so many devices collecting images and sensor information maps are going to be part of the future for businesses.”
Indeed, the mapping market at is expected to grow to $8 billion by 2025. This is in part because a broad range of services are dependent on geo-spatial data.
In addition to traditional cars and self-driving vehicles, emerging urban services such as smart parking, improved traffic information, and delivery information are creating new, and more specific types of needs. For example, many business now use geo-targeting to market products and demand is growing from public agencies such as the military, police and urban planners that need to leverage better geo-location information.
To do street-level mapping tech giants such as Google and Apple send fleets of vehicles roaming the streets with cameras and sensors. It’s a costly and inefficient proposition that makes it difficult to regularly update maps, let alone guarantee widespread coverage.
That is where Mapillary comes in. Solem started the company in 2013 after Apple acquired his previous startup, Polar Rose, which made facial recognition software.
Mapillary first turned to crowdsourcing to enrich mapping data. People can download its app and then upload images from smartphone cameras or GoPros. Mapillary stitches these images together, and its computer vision system then identifies the objects in the images, such as stop signs, lane markings, traffic lights, or other landmarks.