A Holistic Framework for Addressing the World using Machine Learning
Name
A-Holistic-Framework-for-Addressing-the-World-using-Machine-Learning.pdf
Description
Accepted version
Size
18.99 MB
Format
Unknown
Checksum (MD5)
ee74d3a31d5edde8a84f8a8d27fa4078
Author(s)
Demir, Ilke
Hughes, Forest
Raj, Aman
Dhruv, Kaunil
Muddala, Suryanarayana Murthy
Garg, Sanyam
Doo, Barrett
Raskar, Ramesh
Date Issued
June 2018
Publisher
IEEE
Citation
Demir, Ilke, Hughes, Forest, Raj, Aman, Dhruv, Kaunil, Muddala, Suryanarayana Murthy et al. 2018. "A Holistic Framework for Addressing the World using Machine Learning."
Version
Author's final manuscript
Abstract
© 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.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/cvprw.2018.00245
