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- W2901609875 abstract "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." @default.
- W2901609875 created "2018-11-29" @default.
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- W2901609875 date "2018-06-01" @default.
- W2901609875 modified "2023-10-15" @default.
- W2901609875 title "A Holistic Framework for Addressing the World Using Machine Learning" @default.
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- W2901609875 doi "https://doi.org/10.1109/cvprw.2018.00245" @default.
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