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Smart city and geospatiality: Hobart deeply learned

Citation

Aryal, J and Dutta, R, Smart city and geospatiality: Hobart deeply learned, Proceedings of the 2015 IEEE 31st International Conference on Data Engineering Workshops, 13-17 April 2015, Seoul, South Korea, pp. 108-109. ISBN 978-1-4799-8441-1 (2015) [Refereed Conference Paper]

Copyright Statement

Copyright 2015 IEEE

Official URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?...

DOI: doi:10.1109/ICDEW.2015.7129557

Abstract

We propose a cloud computing based big data framework using Deep Neural Networks, to learn urban objects from very high-resolution image in an abstract optimized manner. Automatic recognition of such objects would be essential to minimize big data accessibility issues and increase efficiency of urban dynamics monitoring and planning. We have shown that deep learning could be a way forward towards that complex aim with very high accuracy rates.

Item Details

Item Type:Refereed Conference Paper
Keywords:smart cities, ultra-high resolution, geospatiality, Hobart, IKONOS, GEOBIA, deep learning
Research Division:Engineering
Research Group:Geomatic Engineering
Research Field:Photogrammetry and Remote Sensing
Objective Division:Expanding Knowledge
Objective Group:Expanding Knowledge
Objective Field:Expanding Knowledge in the Earth Sciences
Author:Aryal, J (Dr Jagannath Aryal)
ID Code:97734
Year Published:2015
Deposited By:Geography and Environmental Studies
Deposited On:2015-01-12
Last Modified:2017-10-30
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