| Title |
Smart City and Geospatiality: Hobart Deeply Learned |
| ID_Doc |
39628 |
| Authors |
Aryal, J; Dutta, R |
| Title |
Smart City and Geospatiality: Hobart Deeply Learned |
| Year |
2015 |
| Published |
|
| DOI |
|
| 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. |
| Author Keywords |
smart cities; ultra-high resolution; geospatiality; Hobart; IKONOS; GEOBIA; Deep Learning |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000380392100021 |
| WoS Category |
Computer Science, Theory & Methods; Engineering, Electrical & Electronic |
| Research Area |
Computer Science; Engineering |
| PDF |
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