Geovisual Analytics Environment for Supporting the Resilience of Maritime Surveillance System

International audienceThis paper presents an original approach for supporting the resilience in Maritime Domain Awareness, based on geovisual analytics. While many research projects focus on developing rules for detecting anomalies at by automated means, there is no support to visual exploration led by human operators. We investigate the use of visual methods for analyzing mobility data of ships. Behaviors of interest can be known (modeled) or unknown, asking for various ways of visualizing and studying the information. We assume that supporting the use of geovisual analytics will make the exploration and the analysis process easier, reducing the cognitive load of the tasks led by the actors of maritime surveillance. The detection and the identification of threats at sea are improved by using adequate visualization methods, regarding the context of use. Our suggested framework is based on ontologies for maritime domain awareness and geovisual analytics environments, coupled to rules

Spatial ontologies for detecting abnormal maritime behaviour

International audienceThe 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

Using Ontologies for Proposing Adequate Geovisual Analytics Solutions in the Analysis of Trajectories

International audienceThis paper presents an original approach for supporting the use of geovisual analytics solutions. Many models have been proposed to characterize information visualization methods, but few have been integrated to an intelligent process for supporting user in geo-information usage. Moreover, several new solutions are continuously proposed by research, but few of them are really used in operational world. For instance, the maritime surveillance systems could gain much more identification capabilities of ship behaviors with adequate geovisual analytics solutions. Therefore, we investigated the use of geovisual methods for the analysis of mobility data, such as ship trajectories. We propose a knowledge-based system using ontologies and rules. These allow modeling the domain of geovisual analytics solutions, and their capacities in the exploration and the analysis of trajectories. This system would be used to support users in geovisual analytics of movement, based on their context of use

Système interactif de détection de comportements dynamiques anormaux

International audienceCet article présente une approche d'identification des comportements anormaux dans le cadre de la surveillance maritime. Après avoir rappelé les problèmes de surcharge cognitive qui se posent aux opérateurs du contrôle maritime, l'article présente le processus que les contrôleurs mettent en oeuvre pour analyser une situation. Il détaille ensuite les approches d'identification existantes avant de proposer une démarche originale qui inclut l'humain dans le processus d'aide à la détection de situations anormales

Web-based Geographical Visualization of Container Itineraries

Around 90% of the world cargo is transported in maritime containers, but only around 2% are physically inspected. This opens the possibility for illicit activities. A viable solution is to control containerized cargo through information-based risk analysis. Container route-based analysis has been considered a key factor in identifying potentially suspicious consignments. Essential part of itinerary analysis is the geographical visualization of the itinerary. In the present paper, we present initial work of a web-based system’s realization for interactive geographical visualization of container itinerary.JRC.G.4-Maritime affair

Fusing uncertain knowledge and evidence for maritime situational awareness via Markov Logic Networks

The concepts of event and anomaly are important building blocks for developing a situational picture of the observed environment. We here relate these concepts to the JDL fusion model and demonstrate the power of Markov Logic Networks (MLNs) for encoding uncertain knowledge and compute inferences according to observed evidence. MLNs combine the expressive power of first-order logic and the probabilistic uncertainty management of Markov networks. Within this framework, different types of knowledge (e.g. a priori, contextual) with associated uncertainty can be fused together for situation assessment by expressing unobservable complex events as a logical combination of simpler evidences. We also develop a mechanism to evaluate the level of completion of complex events and show how, along with event probability, it could provide additional useful information to the operator. Examples are demonstrated on two maritime scenarios of rules for event and anomaly detection

Modélisation ontologique pour l'analyse de comportements de navires à risques

International audienceCet article se propose d'étudier les potentialités des ontologies spatiales à la fois comme objet de modélisation, de partage et d'inférence afin d'améliorer l'analyse de comportements des navires à risque. Pour cela, un système de surveillance maritime composée de trois principales ontologies a été développé. Puis, les règles d'inférences nécessaires à la détection des alertes ont été définies par les experts du domaine maritime. Enfin, un moteur de Raisonnement à Partir de Cas permet de déterminer automatiquement les scénarios potentiels issues de l'interprétation d'une situation à risque. Au final, l'objectif est de fournir aux experts du domaine un environnement adapté permettant la modélisation des connaissances spatiales. L'approche adoptée a été mise en application au sein du prototype FishEye