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Maritime anomaly detection: A review
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Skövde Artificial Intelligence Lab (SAIL))ORCID iD: 0000-0003-2900-9335
Lawrence Livermore National Laboratory, Livermore, California, USA.
Directorat e for Space, Security and Migration,Demography, Migration & Governance Unit,European Commission, Joint Research Centre(JRC), Ispra, Italy.
2018 (English)In: Wiley Interdisciplinary Reviews. Data Mining and Knowledge Discovery, ISSN 1942-4787, Vol. 8, no 5, article id e1266Article, review/survey (Refereed) Published
Abstract [en]

The surveillance of large sea areas normally requires the analysis of large volumes of heterogeneous, multidimensional and dynamic sensor data, in order to improve vessel traffic safety, maritime security and to protect the environment. Early detection of conflict situations at sea provides critical time to take appropriate action with, possibly before potential problems occur. In order to provide an overview of the state‐of‐the‐art of research carried out for the analysis of maritime data for situational awareness, this study presents a review of maritime anomaly detection. The found articles are categorized into four groups (a) data, (b) methods, (c) systems, and (d) user aspects. We present a comprehensive summary of the works found in each category, and finally, outline possible paths of investigation and challenges for maritime anomaly detection.

Place, publisher, year, edition, pages
John Wiley & Sons, 2018. Vol. 8, no 5, article id e1266
Keywords [en]
anomaly detection, data mining, maritime anomaly detection, maritime traffic, review, situation awareness
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
URN: urn:nbn:se:his:diva-16182DOI: 10.1002/widm.1266ISI: 000441767200004Scopus ID: 2-s2.0-85051797167OAI: oai:DiVA.org:his-16182DiVA, id: diva2:1247173
Funder
Knowledge Foundation, 20140294Available from: 2018-09-11 Created: 2018-09-11 Last updated: 2018-09-13Bibliographically approved

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Riveiro, Maria

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Citation style
  • apa
  • harvard1
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Output format
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