If we’re going to map the world, we’re not going to do it with ever-greater volumes of elbow grease. There’s just too much work to do. AI and computer vision are helpful assistants in this task, however, as a collaboration between Facebook and OpenStreetMaps has shown, laying down hundreds of thousands of miles of previously unmapped roads in Thailand and other less well-covered countries.
The problem is simply that there’s a whole lot of Earth and only a handful of people actually making maps of it. Sure, Google and Apple have dueling products — but their focus is on businesses in cities and accurate navigation, not including every dirt path and gravel road. Yet for millions of people, those dirt paths and gravel roads are important thoroughfares, and ought to be clearly marked on maps so that they can be reached by other modern services or, you know, get directions. With thousands and thousands of miles not just unmarked but difficult to make out, the mapping community has its work cut out for it. “Most modern algorithms, training sets, and techniques were invented to work for the areas with highly developed infrastructure. In the developing world — for example, Africa, Southeast Asia, Latin America — where roads are not well-defined, maintained, or developed, even the best-trained human eye can struggle to identify and properly classify features,” said Dmitry Kuzhanov, a mapping expert in the ride-sharing industry, in a Facebook blog post about the AI-powered effort. Facebook, of course, wants these far-flung folks to engage with its modern services. Over the last year and a half the company has collaborated with OpenStreetMaps and its users to map 300,000 miles of roads in Thailand, more than doubling what OSM had to begin with. The Map With AI effort resulted in RapiD, a machine learning-enhanced labeling tool that vastly accelerates the process of laying computer-readable roads on top of satellite imagery.![add_ML_road.gif.-1 add ML road.gif. 1](https://i0.wp.com/techcrunch.com/wp-content/uploads/2019/07/add_ML_road.gif.-1.gif?resize=1000%2C559&ssl=1)
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The tool strikes a good balance between suggesting machine-generated features and manual mapping. It gives mappers the final say in what ends up in the map, but helps just enough to both be useful and draw attention to undermapped places. This is definitely going to be a key part of the future of OSM. We can never map the world, and keep it mapped, without assistance from machines. The trick is to find the sweet spot. OSM is a people project, and the map is a reflection of mappers’ interests, skills, biases, etc. That core tenet can never be lost, but it can and must travel along with new horizons in mapping.Of course, unless you want to leave it all to Apple and Google, you could join the ranks of OSM yourself and literally help put some places on the map.