Machine Learning Approaches to Maritime Anomaly Detection

Topics related to safety in maritime transport have become very important over the past decades due to numerous maritime problems putting both human lives and the environment in danger. Recent advances in surveillance technology and the need for better sea traffic protection led to development of automated solutions for detecting anomalies. These solutions are based on generating normality models from data gathered on vessel movement, mostly from AIS. This paper provides a presentation of various machine learning approaches for anomaly detection in the maritime domain. It also addresses potential problems and challenges that could get in the way of successful automation of such systems

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

Marine Heritage Monitoring with High Resolution Survey Tools: ScapaMAP 2001-2006

Archaeologically, marine sites can be just as significant as those on land. Until recently, however, they were not protected in the UK to the same degree, leading to degradation of sites; the difficulty of investigating such sites still makes it problematic and expensive to properly describe, schedule and monitor them. Use of conventional high-resolution survey tools in an archaeological context is changing the economic structure of such investigations however, and it is now possible to remotely but routinely monitor the state of submerged cultural artifacts. Use of such data to optimize expenditure of expensive and rare assets (e.g., divers and on-bottom dive time) is an added bonus. We present here the results of an investigation into methods for monitoring of marine heritage sites, using the remains of the Imperial German Navy (scuttled 1919) in Scapa Flow, Orkney as a case study. Using a baseline bathymetric survey in 2001 and a repeat bathymetric and volumetric survey in 2006, we illustrate the requirements for such surveys over and above normal hydrographic protocols and outline strategies for effective imaging of large wrecks. Suggested methods for manipulation of such data (including processing and visualization) are outlined, and we draw the distinction between products for scientific investigation and those for outreach and education, which have very different requirements. We then describe the use of backscatter and volumetric acoustic data in the investigation of wrecks, focusing on the extra information to be gained from them that is not evident in the traditional bathymetric DTM models or sounding point-cloud representations of data. Finally, we consider the utility of high-resolution survey as part of an integrated site management policy, with particular reference to the economics of marine heritage monitoring and preservation

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-Temporal Data Mining for Ocean Science: Data, Methodologies, and Opportunities

With the increasing amount of spatial-temporal~(ST) ocean data, numerous spatial-temporal data mining (STDM) studies have been conducted to address various oceanic issues, e.g., climate forecasting and disaster warning. Compared with typical ST data (e.g., traffic data), ST ocean data is more complicated with some unique characteristics, e.g., diverse regionality and high sparsity. These characteristics make it difficult to design and train STDM models. Unfortunately, an overview of these studies is still missing, hindering computer scientists to identify the research issues in ocean while discouraging researchers in ocean science from applying advanced STDM techniques. To remedy this situation, we provide a comprehensive survey to summarize existing STDM studies in ocean. Concretely, we first summarize the widely-used ST ocean datasets and identify their unique characteristics. Then, typical ST ocean data quality enhancement techniques are discussed. Next, we classify existing STDM studies for ocean into four types of tasks, i.e., prediction, event detection, pattern mining, and anomaly detection, and elaborate the techniques for these tasks. Finally, promising research opportunities are highlighted. This survey will help scientists from the fields of both computer science and ocean science have a better understanding of the fundamental concepts, key techniques, and open challenges of STDM in ocean

The Maritime Domain Awareness Center– A Human-Centered Design Approach

This paper contends that Maritime Domain Awareness Center (MDAC) design should be a holistic approach integrating established knowledge about human factors, decision making, cognitive tasks, complexity science, and human information interaction. The design effort should not be primarily a technology effort that focuses on computer screens, information feeds, display technologies, or user interfaces. The existence of a room with access to vast amounts of information and wall-to-wall video screens of ships, aircraft, weather data, and other regional information does not necessarily correlate to possessing situation awareness. Fundamental principles of human-centered information design should guide MDAC design and technology selection, and it is imperative that they be addressed early in system development. The design approach should address the reason and purpose for a given MDAC. Subsequent design efforts should address ergonomic interaction with information – the relationship of the brain to the information ecosystem provided by the MDAC, and the cognitive science of situation awareness and decision making. This understanding will guide technology functionality. The system user and decision maker should be the focus of the information design specifications, and this user population must participate and influence the information design. Accordingly, this paper provides a “design gestalt” by which to approach the design and development of an MDAC

: Application à la sécurité et sûreté maritime

Traduction de l'article "High-Level Taxonomy of Geovisual Analytics Tasks for Maritime Surveillance" : http://hal-ensmp.archives-ouvertes.fr/hal-00856714International audienceLe contrôle de la sécurité et la sûreté maritime requiert une surveillance constante du trafic en mer, dans laquelle interviennent de nombreux acteurs : contrôleurs, analystes, etc. Des systèmes visuels (systèmes de surveillance maritimes) permettent de gérer les dangers potentiels, les navires suspects et suivre situation générale du trafic maritime. Néanmoins, ces systèmes présentent de lourdes charges cognitives, et aucun outil d'analyse n'est disponible pour faciliter ces nombreuses missions. Au cours des dernières années, le domaine de l'analyse géovisuelle s'est montré très efficace pour l'analyse de grandes quantités de données hétérogènes. De nouvelles méthodes ont été développées afin de faciliter l'analyse et la découverte de connaissances, par la visualisation. Pourtant, l'utilisation de tels environnements avancés peut s'avérer trop complexe, selon le profil de l'utilisateur, ou non adaptées à certaines utilisations. Il est donc fondamental d'étudier les tâches à mener lors de l'analyse de comportements à risques, afin d'utiliser des solutions adaptées. Dans ce papier, nous proposons une étude des tâches d'exploration et d'analyse du trafic maritime par la visualisation d'information géographique. Par la suite, ces connaissances seront utilisées dans un système à base de connaissances pour une aide à l'analyse des données de trafic