Robocodes: Towards Generative Street Addresses from Satellite Imagery
Name
Demir_Robocodes_Towards_Generative_CVPR_2017_paper.pdf
Description
Published version
Size
12.59 MB
Format
Unknown
Checksum (MD5)
e98f2afa751de358b4b0102cca8cfdda
Author(s)
Demir, Ilke
Hughes, Forest
Raj, Aman
Tsourides, Kleovoulos
Ravichandran, Divyaa
Murthy, Suryanarayana
Dhruv, Kaunil
Garg, Sanyam
Malhotra, Jatin
Doo, Barrett
Date Issued
July 2017
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
2017. "Robocodes: Towards Generative Street Addresses from Satellite Imagery."
Version
Final published version
Abstract
© 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.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/CVPRW.2017.192
