smart Emergency Response System (smartERS) – the Oil Spill use case

Thanks to the huge progress within the last 50 years in Earth Observation, Geospatial science and ICT technology, mankind is facing, for the first time, the opportunity to effectively respond to natural and artificial emergencies such as: earthquake, flood, oil spill, etc. Responding to an emergency requires to find, access, exchange, and of course understand many types of geospatial information provided by several types of sensors. Majors oil spills emergencies as, the Gulf of Mexico (Macondo/Deepwater Horizon) in 2010, the sinking of the oil tanker Prestige in 2002, have offered lessons learned and identified challenges to be addressed. Interoperability provides the principles and technologies to address those challenges. Since years interoperability has been developing based on traditional Service Oriented Architecture, request/response communication style, and implemented through Spatial Data Infrastructures. The experience handling oil spill responses shows that emergency services based on SDIs have some limitations, mainly due to their real-time peculiarity. Moreover despite the effort that Private Sector and Public Administration have been putting since years, the goal to provide an exhaustive picture of the situation during an Emergency Response is still far to be reached. We argue that to achieve this goal, we have to frame the problem in a different way. Emergency Response is not just sensing; it should be smart enough to encompass intelligent actions such as, automatically and dynamically acquire context driven information. The gaol of this paper is to define what a “smart Emergency Response System” (smartERS) should be.JRC.G.3-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

Data mining for anomaly detection in maritime traffic data

For the past few years, oceans have become once again, an important means of communication and transport. In fact, traffic density throughout the globe has suffered a substantial growth, which has risen some concerns. With this expansion, the need to achieve a high Maritime Situational Awareness (MSA) is imperative. At the present time, this need may be more easily fulfilled thanks to the vast amount of data available regarding maritime traffic. However, this brings in another issue: data overload. Currently, there are so many data sources, so many data to obtain information from, that the operators cannot handle it. There is a pressing need for systems that help to sift through all the data, analysing and correlating, helping in this way the decision making process. In this dissertation, the main goal is to use different sources of data in order to detect anomalies and contribute to a clear Recognised Maritime Picture (RMP). In order to do so, it is necessary to know what types of data exist and which ones are available for further analysis. The data chosen for this dissertation was Automatic Identification System (AIS) and Monitorização Contínua das Atividades da Pesca (MONICAP) data, also known as Vessel Monitoring System (VMS) data. In order to store 1 year worth of AIS and MONICAP data, a PostgreSQL database was created. To analyse and draw conclusions from the data, a data mining tool was used, namely, Orange. Tests were conducted in order to assess the correlation between data sources and find anomalies. The importance of data correlation has never been so important and with this dissertation the aim is to show that there is a simple and effective way to get answers from great amounts of data.Nos últimos anos, os oceanos tornaram-se, mais uma vez, um importante meio de comunicação e transporte. De facto, a densidade de tráfego global sofreu um crescimento substancial, o que levantou algumas preocupações. Com esta expansão, a necessidade de atingir um elevado Conhecimento Situacional Marítimo (CSM) é imperativa. Hoje em dia, esta necessidade pode ser satisfeita mais facilmente graças à vasta quantidade de dados disponíveis de tráfego marítimo. No entanto, isso leva a outra questão: sobrecarga de dados. Atualmente existem tantas fontes de dados, tantos dados dos quais extrair informação, que os operadores não conseguem acompanhar. Existe uma necessidade premente para sistemas que ajudem a escrutinar todos os dados, analisando e correlacionando, contribuindo desta maneira ao processo de tomada de decisão. Nesta dissertação, o principal objetivo é usar diferentes fontes de dados para detetar anomalias e contribuir para uma clara Recognised Maritime Picture (RMP). Para tal, é necessário saber que tipos de dados existem e quais é que se encontram disponíveis para análise posterior. Os dados escolhidos para esta dissertação foram dados Automatic Identification System (AIS) e dados de Monitorização Contínua das Atividades da Pesca (MONICAP), também conhecidos como dados de Vessel Monitoring System (VMS). De forma a armazenar dados correspondentes a um ano de AIS e MONICAP, foi criada uma base de dados em PostgreSQL. Para analisar e retirar conclusões, foi utilizada uma ferramenta de data mining, nomeadamente, o Orange. De modo a que pudesse ser avaliada a correlação entre fontes de dados e serem detetadas anomalias foram realizados vários testes. A correlação de dados nunca foi tão importante e pretende-se com esta dissertação mostrar que existe uma forma simples e eficaz de obter respostas de grandes quantidades de dado

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

Situational awareness in the marine towing industry

Loss of situational awareness has been cited as the most common cause of accidents in the maritime industry. However, current research has not proven beneficial in determining the factors that underpin situational awareness. This study examined situational awareness among licensed captains and pilots of the inland towing industry. Twenty towboat captains, who operate on the Gulf Intracoastal Waterway (GIWW) from New Orleans, LA to Houston, TX and who represented 16 towing vessel companies, were interviewed and participated in an integrated survey that explored fatigue, communication, dangerous drugs and alcohol, social stress, and mental workload. These factors were believed to have a direct influence on situational awareness among towing vessel captains. These factors were compared and resulted in the identification of mental workload as the predominant factor affecting situational awareness. Upon further examination of mental workload, it was found that loss of situational awareness is likely to occur when a towing vessel captain is distracted by a cell phone conversation with their company or steers his or her tow in the vicinity of recreational vessels. In addition, the results also indicated complacency potential, a function of attitude toward automation and perception of mental workload. It was concluded that outreach and education to recreational boaters, establishing procedures for business conversations, navigation simulators, drills, and strategic thinking could be effective counter-measures against the loss of situational awareness induced by mental workload. Further research that measures the impact of those distractions or examines the effectiveness of such counter-measures in reducing the potential for loss of situational awareness is neede

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