Ontology-aided feature correlation for multi-modal urban sensing
Conference Proceeding Article
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.
Event Detection, Information Fusion, Multi-Modal Sensing
OS and Networks
Data Management and Analytics
Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR VII, Baltimore, United States; 2016 April 18-20
City or Country
MISRA, Archan; LANTRA, Mohamed Zaman; and KASTHURI JAYARAJAH, .
Ontology-aided feature correlation for multi-modal urban sensing. (2014). Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR VII, Baltimore, United States; 2016 April 18-20. 9831,. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3582