An estimated 4 billion people in the world lack a physical address. Researchers at the MIT Media Lab and Facebook are now proposing a new way to address the unaddressed: with machine learning. From a report: The team first trained a deep-learning algorithm to extract the road pixels from satellite images. Another algorithm connected the pixels together into a road network. The system analyzed the density and shape of the roads to segment the network into different communities, and the densest cluster was labeled as the city center. The regions around the city center were divided into north, south, east, and west quadrants, and streets were numbered and lettered according to their orientation and distance from the center.
When they compared their final results with a random sample of unmapped regions whose streets had been labeled manually, their approach successfully addressed more than 80% of the populated areas, improving coverage compared with Google Maps or OpenStreetMaps. This isn’t the only way to automate the creation of addresses. The organization what3words generates a unique three-word combination for every 3-by-3-meter square on a global grid. The scheme has already been adopted in regions of South Africa, Turkey, and Mongolia by national package delivery services, local hospitals, and regional security teams. But Ilke Demir, a researcher at Facebook and one of the creators of the new system, says its main advantage is that it follows existing road topology and helps residents understand how two addresses relate to one another.
of this story at Slashdot.
Source:: Slashdot