The AI-ARC proposal presents a highly innovative and user-friendly artificial intelligence (AI) based platform known as the Virtual Control Room (VCR). Due to the vast amounts of information collected the potential for information overload is real. This reality can complicate the operational picture; reduce situational awareness and often results in delayed and impaired decision-making. On the other hand, areas such as the Arctic Sea suffer from a lack of communication, surveillance data and rescue assets and without action taken to address these vulnerabilities, the consequences are potentially dramatic in terms of accidents, pollution, border infringements and criminal activities. The AI-ARC VCR supports all these challenges by applying AI, machine-learning and virtual reality (VR) technologies to filter numerous validated and statistical data streams and databases to a user-friendly interface. The VCR improves situational awareness by assisting end users to customize a “smart” operational picture. The VCR will permit users to specify their preferences in terms of threat levels, abnormal behaviour, interoperability and risk management by flagging detected anomalies with confidence and providing threat or risk levels according to a predefined model based on user preferences. This means that users can create awareness for their own purposes that reflects their needs without increasing their workload.
AI-ARC‘s principal objectives align fully with the H2020 BES-SU-open, and are of crucial relevance to it. The Virtual Control Room (VCR) has the power to greatly improve maritime situational awareness, decision-making, communication, available rescue resources, and thus the safety of all maritime actors, particularly in the Arctic Sea. Furthermore, the enhanced communication and collaboration provided by AI ARC’s innovative technology encourages and enables further development of symbiotic services and fosters much needed Arctic cooperation.
TREE plays two main roles within the technical fields: contribution to the design, development and deployment of the data platform as well as implementation of AI-based algorithms for anomaly detection and prediction.
This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 101021271
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