Enhancing automatic maritime surveillance systems with visual information

Automatic surveillance systems for the maritime domain are becoming more and more important due to a constant increase of naval traffic and to the simultaneous reduction of crews on decks. However, available technology still provides only a limited support to this kind of applications. In this paper, a modular system for intelligent maritime surveillance, capable of fusing information from heterogeneous sources, is described. The system is designed to enhance the functions of the existing Vessel Traffic Services systems and to be deployable in populated areas, where radar-based systems cannot be used due to the high electromagnetic radiation emissions. A quantitative evaluation of the proposed approach has been carried out on a large and publicly available data set of images and videos, collected from multiple real sites, with different light, weather, and traffic conditions

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

Technology and maritime security in Africa : opportunities and challenges in Gulf of Guinea

This research was supported by funding from the St Andrews Research Internship Scheme (StARIS).Maritime security threats undermine safety and security at sea and, in turn, coastal states’ efforts to harness the resources in their maritime domain. This assertion is true for coastal states and Small Island Developing States (SIDS) on the African continent, where limited maritime enforcement capabilities have increased security threats at sea, such as illegal, unreported and unregulated fishing, piracy and armed robbery at sea, toxic waste dumping and other illicit activities. African navies and their foreign partners are taking advantage of the opportunities that technology provides to improve safety and security. Technology has led to the identification of criminals at sea, their capture and prosecution, making it crucial in enhancing maritime security. As such, the merits of its use for maritime security are undeniable. However, using technology comes with challenges that need to be considered. With this in mind, our research makes an original contribution by exploring the opportunities for using technology to advance maritime safety and security in Africa, successes and challenges with an emphasis on the Gulf of Guinea region. Drawing from questionnaire data from maritime law enforcement personnel, agencies supporting the implementation of the Yaoundé Code of Conduct (2013), and a review of relevant literature and policy documents, we contend that technology has significantly improved maritime domain awareness and the effective implementation of maritime safety and security in the Gulf of Guinea. However, addressing existing limitations and enhancing human capacity is imperative to sustain this progress.Publisher PDFPeer reviewe

Detecting Intentional AIS Shutdown in Open Sea Maritime Surveillance Using Self-Supervised Deep Learning

In maritime traffic surveillance, detecting illegal activities, such as illegal fishing or transshipment of illicit products is a crucial task of the coastal administration. In the open sea, one has to rely on Automatic Identification System (AIS) message transmitted by on-board transponders, which are captured by surveillance satellites. However, insincere vessels often intentionally shut down their AIS transponders to hide illegal activities. In the open sea, it is very challenging to differentiate intentional AIS shutdowns from missing reception due to protocol limitations, bad weather conditions or restricting satellite positions. This paper presents a novel approach for the detection of abnormal AIS missing reception based on self-supervised deep learning techniques and transformer models. Using historical data, the trained model predicts if a message should be received in the upcoming minute or not. Afterwards, the model reports on detected anomalies by comparing the prediction with what actually happens. Our method can process AIS messages in real-time, in particular, more than 500 Millions AIS messages per month, corresponding to the trajectories of more than 60 000 ships. The method is evaluated on 1-year of real-world data coming from four Norwegian surveillance satellites. Using related research results, we validated our method by rediscovering already detected intentional AIS shutdowns.Comment: IEEE Transactions on Intelligent Transportation System