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More than half of the world's roads lack adequate street addressing systems.Lack of addresses is even more visible in daily lives of people in developingcountries. We would like to object to the assumption that having an address isa luxury, by proposing a generative address design that maps the world inaccordance with streets. The addressing scheme is designed considering severaltraditional street addressing methodologies employed in the urban developmentscenarios around the world. Our algorithm applies deep learning to extractroads from satellite images, converts the road pixel confidences into a roadnetwork, partitions the road network to find neighborhoods, and labels theregions, roads, and address units using graph- and proximity-based algorithms.We present our results on a sample US city, and several developing cities,compare travel times of users using current ad hoc and new complete addresses,and contrast our addressing solution to current industrial and open geocodingalternatives.

Addressing the Invisible: Street Address Generation for Developing Countries with Deep Learning