Ontology-aided feature correlation for multi-modal urban sensing

Author

Archan MISRA, Singapore Management UniversityFollow
Zaman LANTRA, University of Moratuwa
Kasthuri JAYARAJAH, Singapore Management UniversityFollow

Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

4-2016

Abstract

The paper explores the use of correlation across features extracted from different sensing channels to help in urban situational understanding. We use real-world datasets to show how such correlation can improve the accuracy of detection of city-wide events by combining metadata analysis with image analysis of Instagram content. We demonstrate this through a case study on the Singapore Haze. We show that simple ontological relationships and reasoning can significantly help in automating such correlation-based understanding of transient urban events.

Keywords

Event Detection, Information Fusion, Multi-Modal Sensing

Discipline

Asian Studies | Environmental Sciences | Numerical Analysis and Scientific Computing | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

Proceedings of SPIE: Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR VII, Baltimore, 2016 April 18-20

Volume

9831

First Page

9:1

Last Page

10

ISBN

9781510600720

Identifier

10.1117/12.2225143

Publisher

SPIE

City or Country

Bellingham, WA

Citation

MISRA, Archan; LANTRA, Zaman; and JAYARAJAH, Kasthuri. Ontology-aided feature correlation for multi-modal urban sensing. (2016). Proceedings of SPIE: Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR VII, Baltimore, 2016 April 18-20. 9831, 9:1-10.
Available at: https://ink.library.smu.edu.sg/sis_research/3582

Copyright Owner and License

Authors

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.

Additional URL

https://doi.org/10.1117/12.2225143