Generative Street Addresses from Satellite Imagery
Name
ijgi-07-00084.pdf
Size
87.68 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Demir, İlke
Hughes, Forest
Raj, Aman
Dhruv, Kaunil
Muddala, Suryanarayana Murthy
Garg, Sanyam
Doo, Barrett
Muddala, Suryanarayana
Raskar, Ramesh
Date Issued
March 2018
Journal
ISPRS International Journal of Geo-Information
Publisher
MDPI AG
Citation
Demir, İlke et al. "Generative Street Addresses from Satellite Imagery." ISPRS International Journal of Geo-Information 7, 3 (March 2018): 84 © 2018 The Authors
Version
Final published version
Abstract
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 geocoding
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
http://dx.doi.org/10.3390/ijgi7030084
