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International audience ; The upsurge in piracy and the impact of recent environmental disasters have highlighted the need to improve maritime surveillance. Governmental and private initiatives have developed monitoring systems with improved acquisition and analysis capabilities. These systems rely on one major component, namely the detection of abnormal ship behaviour. This implies a detailed formalisation of expert knowledge. However, the quantity of data, the complexity of situations, the failure to take into account their spatial characteristics and the potential for the same scenario to be interpreted in different ways have proved to be significant problems. We therefore propose a new prototype for the analysis of abnormal ship behaviour. The system is based on a spatial ontology associated with a geographical inference engine. It automatically identifies suspicious vessels and associates them with probable behaviours defined by operational staff.

International audience ; The upsurge in piracy and the impact of recent environmental disasters have highlighted the need to improve maritime surveillance. Governmental and private initiatives have developed monitoring systems with improved acquisition and analysis capabilities. These systems rely on one major component, namely the detection of abnormal ship behaviour. This implies a detailed formalisation of expert knowledge. However, the quantity of data, the complexity of situations, the failure to take into account their spatial characteristics and the potential for the same scenario to be interpreted in different ways have proved to be significant problems. We therefore propose a new prototype for the analysis of abnormal ship behaviour. The system is based on a spatial ontology associated with a geographical inference engine. It automatically identifies suspicious vessels and associates them with probable behaviours defined by operational staff.