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 developing
countries. We would like to object to the assumption that having an address is
a luxury, by proposing a generative address design that maps the world in
accordance with streets. The addressing scheme is designed considering several
traditional street addressing methodologies employed in the urban development
scenarios around the world. Our algorithm applies deep learning to extract
roads from satellite images, converts the road pixel confidences into a road
network, partitions the road network to find neighborhoods, and labels the
regions, 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 geocoding
alternatives.

本研究提出了一种基于深度学习和图像区分的地址生成方案，该方案可以通过从卫星图像中提取道路信息、划分社区、标记区域与地址单位等步骤，映射全球道路，并与当前工业和开源地址编码方案进行了比较。