English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

The global expansion of maritime activities and the development of theAutomatic Identification System (AIS) have driven the advances in maritimemonitoring systems in the last decade. Monitoring vessel behavior isfundamental to safeguard maritime operations, protecting other vessels sailingthe ocean and the marine fauna and flora. Given the enormous volume of vesseldata continually being generated, real-time analysis of vessel behaviors isonly possible because of decision support systems provided with event andanomaly detection methods. However, current works on vessel event detection aread-hoc methods able to handle only a single or a few predefined types of vesselbehavior. Most of the existing approaches do not learn from the data andrequire the definition of queries and rules for describing each behavior. Inthis paper, we discuss challenges and opportunities in classical machinelearning and deep learning for vessel event and anomaly detection. We hope tomotivate the research of novel methods and tools, since addressing thesechallenges is an essential step towards actual intelligent maritime monitoringsystems.

Challenges in Vessel Behavior and Anomaly Detection: From Classical Machine Learning to Deep Learning