Mining Vessel Tracking Data for Maritime Domain Applications
作者： Alfredo Alessandrini , Marlene Alvarez , Harm Greidanus , Vincenzo Gammieri , Virginia Fernandez Arguedas
关键词: Earth observation 、 Data analysis 、 Risk assessment 、 Data science 、 Big data 、 Computer science 、 Fisheries management 、 Spatial planning 、 Identification (information) 、 Anomaly detection 、 Knowledge extraction
摘要: The growing number of remote sensing systems and ship reporting technologies (e.g. Automatic Identification System, Long Range Tracking, radar tracking, Earth Observation) are generating an overwhelming amount spatio-temporal geographically distributed data related to vessels their movements. Research on reliable mining techniques has proven essential the discovery knowledge from such increasingly available information traffic at sea. Data driven very recently demonstrated its value in fields that go beyond original maritime safety security remits data. They include, but not limited to, fisheries management, spatial planning, gridding emissions, mapping activities sea, risk assessment offshore platforms, trade indicators. extraction useful Big is thus a key element providing operational authorities, policy-makers scientists with supporting tools understand what happening sea improve knowledge. This work will provide survey recent JRC research relevant automatic anomaly detection domain. mining, analytics predictive analysis examples introduced using real In addition, this paper presents approaches detect anomalies messages unexpected behaviours
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