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

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

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

Maritime data integration and analysis: Recent progress and research challenges

The correlated exploitation of heterogeneous data sources offering very large historical as well as streaming data is important to increasing the accuracy of computations when analysing and predicting future states of moving entities. This is particularly critical in the maritime domain, where online tracking, early recognition of events, and real-time forecast of anticipated trajectories of vessels are crucial to safety and operations at sea. The objective of this paper is to review current research challenges and trends tied to the integration, management, analysis, and visualization of objects moving at sea as well as a few suggestions for a successful development of maritime forecasting and decision-support systems

Development of a Web-Based Geographical Information System for Interactive Visualization and Analysis of Container Itineraries

The paper describes an advanced prototype of a web-based geographical information system for user-friendly, interactive and efficient visualization of containers travelling over the world. The prototype uses ConTraffic Oracle Data Base (DB), where more than 300 000 container’s events are archived daily. The DB contains currently around one billion container movements. In addition, geographical data about the used locations/ports was collected and stored in the same DB on which the prototype is implemented. The prototype system provides users with container traffic information for specific date range, presented in interactive geographical and tabular mode. As a result, the prototype makes efficient visualization for easy visual analysis of container movements and status. The system used in this study gathers in quasi real-time online data from open sources, processes and stores it in DB. Using the proposed GIS application the user can access any time the DB and review on a map the itinerary of a specific container in specific date range, interact with the geographical presentation to receive specific details for the container for the used ports and review the itinerary details in interactive tabular presentation.JRC.G.4-Maritime affair

GeoLocSI – Web-Based GIS for Verification and Modification of Data Stored in Data Base

Currently there are thousand container events happening daily on more than 20 000 locations in the World. Some of these locations are big international ports and others are just little cities with not precise coordinates in the free available Data Bases (DB). Verification and validation these locations are at the same time a very important task and a challenging one. This paper describes the development of a web-based geographical information system for assisting in verifying and modifying geographical data in DB by interactive intuitive GIS technique. For the proper work of the system, first we collected geographical data for container ports from different open sources according to the known container ports’ names from our ConTraffic System. Then we stored it in a dataset in our DB and we created a map-based application which allows us to see not only the data in tabular view but also the geographical position of the ports over a map. Using this web-based application all the data can be modified quite easy, including the geographical coordinates. They can be modified directly by just typing the correct coordinates or by interactive way (drag the graphical object to the correct geographical position on the map).JRC.G.4-Maritime affair

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

Identification of vessel anomaly behavior using support vector machines and Bayesian networks

In this work, a model based on Support Vector Machines (SVMs) classification to identify vessel anomaly behavior have been proposed and implemented, and the result is compared to Bayesian Networks (BNs). The works have been done using the real world Automated Identification System (AIS) vesselreporting data. SVMs can achieve higher accuracy compared to BNs in both memory-test and blind-test. The effect of holdout method which is partitioned size of training and testing data set on the accuracy result were also investigated in this study. The proposed classifier demonstrated to be a viable tool for identifying the vessel anomaly behavior by its high accuracy