In the last decade, Artificial Intelligence (AI) has emerged as a powerful technology that profoundly impacts our society. AI-based solutions have addressed complex problems in various domains, including healthcare, finance, and transportation. In recent years, AI has also been recognized as a key enabler in enhancing Maritime Domain Awareness (MDA) and improving the safety and security of naval transportation. Modern maritime surveillance systems can integrate multiple data types and produce massive amounts of information. However, maritime traffic management decisions are still primarily based on human expertise. By harnessing the power of AI, operators can be assigned more complex analytical tasks, and intelligent computer systems can eliminate incomplete, inaccurate, or biased information. This chapter explores AI's potential to enhance various MDA operations, including defending sovereignty, protecting infrastructure, countering terrorism, and detecting illegal activities. It reflects on the specificity of different maritime agencies and presents the main data types and sources used in maritime intelligence gathering. Furthermore, this chapter describes the main trends in using AI algorithms in naval anomaly detection, collision avoidance, path planning, and route estimation. The chapter concludes by discussing the challenges and opportunities of utilizing AI in the maritime domain, including ethical and legal considerations, data availability, and interoperability issues. The results of this study are expected to provide valuable insights for researchers, practitioners, and policymakers interested in utilizing AI to enhance MDA and improve the safety and security of naval transportation.


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    Titel :

    Artificial Intelligence in Maritime Domain Awareness Applications: Trends and Prospects


    Weitere Titelangaben:

    Intel.Syst.Ref.Library


    Beteiligte:
    Ivascu, Larisa (Herausgeber:in) / Cioca, Lucian-Ionel (Herausgeber:in) / Doina, Banciu (Herausgeber:in) / Filip, Florin Gheorghe (Herausgeber:in) / Pohontu, Alexandru (Autor:in) / Ermolai, Vasile (Autor:in)

    Erschienen in:

    Digital Transformation ; Kapitel : 10 ; 193-204


    Erscheinungsdatum :

    22.08.2024


    Format / Umfang :

    12 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch