Generative Street Addresses from Satellite Imagery

We describe our automatic generative algorithm to create street addresses from satellite images by learning and labeling roads, regions, and address cells. Currently, 75% of the world’s roads lack adequate street addressing systems. Recent geocoding initiatives tend to convert pure latitude and longitude information into a memorable form for unknown areas. However, settlements are identified by streets, and such addressing schemes are not coherent with the road topology. Instead, we propose a generative address design that maps the globe in accordance with streets. Our algorithm starts with extracting roads from satellite imagery by utilizing deep learning. Then, it uniquely labels the regions, roads, and structures using some graph- and proximity-based algorithms. We also extend our addressing scheme to (i) cover inaccessible areas following similar design principles; (ii) be inclusive and flexible for changes on the ground; and (iii) lead as a pioneer for a unified street-based global geodatabase. We present our results on an example of a developed city and multiple undeveloped cities. We also compare productivity on the basis of current ad hoc and new complete addresses. We conclude by contrasting our generative addresses to current industrial and open solutions. Keywords: road extraction; remote sensing; satellite imagery; machine learning; supervised learning; generative schemes; automatic geocodin

A Holistic Framework for Addressing the World using Machine Learning

© 2018 IEEE. Millions of people are disconnected from basic services due to lack of adequate addressing. We propose an automatic generative algorithm to create street addresses from satellite imagery. Our addressing scheme is coherent with the street topology, linear and hierarchical to follow human perception, and universal to be used as a unified geocoding system. Our algorithm starts with extracting road segments using deep learning and partitions the road network into regions. Then regions, streets, and address cells are named using proximity computations. We also extend our addressing scheme to cover inaccessible areas, to be flexible for changes, and to lead as a pioneer for a unified geodatabase

Robocodes: Towards Generative Street Addresses from Satellite Imagery

© 2017 IEEE. We describe our automatic generative algorithm to create street addresses (Robocodes) from satellite images by learning and labeling regions, roads, and blocks. 75% of the world lacks street addresses [12]. According to the United Nations, this means 4 billion people are 'invisible'. Recent initiatives tend to name unknown areas by geocoding, which uses latitude and longitude information. Nevertheless settlements abut roads and such addressing schemes are not coherent with the road topology. Instead, our algorithm starts with extracting roads and junctions from satellite imagery utilizing deep learning. Then, it uniquely labels the regions, roads, and houses using some graph- and proximity-based algorithms. We present our results on both cities in mapped areas and in developing countries. We also compare productivity based on current ad-hoc and new complete addresses. We conclude with contrasting our generative addresses to current industrial and open solutions