12 May 2016 Ontology-aided feature correlation for multi-modal urban sensing
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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.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Archan Misra, Archan Misra, Zaman Lantra, Zaman Lantra, Kasthuri Jayarajah, Kasthuri Jayarajah, "Ontology-aided feature correlation for multi-modal urban sensing", Proc. SPIE 9831, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR VII, 98310A (12 May 2016); doi: 10.1117/12.2225143; https://doi.org/10.1117/12.2225143


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