Abstract
The research presented in this paper aims to show the deployment and use of advanced technologies towards processing surveillance data for the detection of events, contributing to maritime situation awareness via trajectories’ detection, synopses generation and semantic enrichment of trajectories. We first introduce the context of the maritime domain and then the main principles of the big data architecture developed so far within the European funded H2020 datAcron project. From the integration of large maritime trajectory datasets, to the generation of synopses and the detection of events, the main functions of the datAcron architecture are developed and discussed. The potential for detection and forecasting of complex events at sea is illustrated by preliminary experimental results.
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Acknowledgments
This work was supported by project datACRON, which has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 687591.
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Vouros, G.A. et al. (2018). Increasing Maritime Situation Awareness via Trajectory Detection, Enrichment and Recognition of Events. In: R. Luaces, M., Karimipour, F. (eds) Web and Wireless Geographical Information Systems. W2GIS 2018. Lecture Notes in Computer Science(), vol 10819. Springer, Cham. https://doi.org/10.1007/978-3-319-90053-7_13
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DOI: https://doi.org/10.1007/978-3-319-90053-7_13
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