A Holistic Framework for Addressing the World Using Machine Learning

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,...

Full description

Saved in:
Bibliographic Details
Main Authors: Demir, Ilke, Hughes, Forest, Raj, Aman, Dhruv, Kaunil, Muddala, Suryanarayana Murthy, Garg, Sanyam, Doo, Barrett, Raskar, Ramesh
Format: Conference Proceeding
Language:English
Subjects:
Computer vision
Conferences
Image segmentation
Roads
Satellites
Topology
Urban areas
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2160-7516
DOI:10.1109/CVPRW.2018.00245