With the increasing demands for safety, efficiency, and sustainability in global shipping, Automatic Identification System (AIS) data plays an increasingly important role in maritime monitoring. AIS data contains spatial-temporal variation patterns of vessels that hold significant research value in the marine domain. However, due to its massive scale, the full potential of AIS data has long remained untapped. With its powerful sequence modeling capabilities, particularly its ability to capture long-range dependencies and complex temporal dynamics, the Transformer model has emerged as an effective tool for processing AIS data. Therefore, this paper reviews the research on Transformer-based AIS data-driven maritime monitoring, providing a comprehensive overview of the current applications of Transformer models in the marine field. The focus is on Transformer-based trajectory prediction methods, behavior detection, and prediction techniques. Additionally, this paper collects and organizes publicly available AIS datasets from the reviewed papers, performing data filtering, cleaning, and statistical analysis. The statistical results reveal the operational characteristics of different vessel types, providing data support for further research on maritime monitoring tasks. Finally, we offer valuable suggestions for future research, identifying two promising research directions. Datasets are available at https://github.com/eyesofworld/Maritime-Monitoring.

Intelligent maritime, as an essential component of smart ocean construction, deeply integrates advanced artificial intelligence technology and data analysis methods, which covers multiple aspects such as smart vessels, route optimization, safe navigation, aiming to enhance the efficiency of ocean resource utilization and the intelligence of transportation networks. However, the complex and dynamic maritime environment, along with diverse and heterogeneous large-scale data sources, present challenges for real-time decision-making in intelligent maritime. In this paper, We propose KUNPENG, the first-ever embodied large model for intelligent maritime in the smart ocean construction, which consists of six systems. The model perceives multi-source heterogeneous data for the cognition of environmental interaction and make autonomous decision strategies, which are used for intelligent vessels to perform navigation behaviors under safety and emergency guarantees and continuously optimize power to achieve embodied intelligence in maritime. In comprehensive maritime task evaluations, KUNPENG has demonstrated excellent performance.

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FrontM is on a mission to be the EDGE-intelligent app marketplace where the world goes to connect, inform and care for remote teams and customers. The innovation focuses on overcoming digital poverty in remote and isolated environments, such as the Blue Economy. The World Bank defines the blue economy as the "sustainable use of ocean resources for economic growth, improved livelihoods and jobs while preserving the health of the ocean ecosystem." FrontM's initial use cases include the maritime commercial shipping market, particularly transforming shore-ship team collaboration, automation of workflows, crew safety and welfare. FrontM is proud to be recognised by Innovate UK and receiving a grant to study the feasibility of integration of Edge AI enablement technology from Hammer Of The Gods (HOT-G).

Shipping is critical to our modern life. As goods criss-cross around the globe, demand is higher than ever. But there's also a push to make these logistics smarter and more eco friendly. AI can help with that. So, to that end, here's a quick guide to Artificial Intelligence in shipping and how AI can work for you.

The global expansion of maritime activities and the development of the Automatic Identification System (AIS) have driven the advances in maritime monitoring systems in the last decade. Monitoring vessel behavior is fundamental to safeguard maritime operations, protecting other vessels sailing the ocean and the marine fauna and flora. Given the enormous volume of vessel data continually being generated, real-time analysis of vessel behaviors is only possible because of decision support systems provided with event and anomaly detection methods. However, current works on vessel event detection are ad-hoc methods able to handle only a single or a few predefined types of vessel behavior. Most of the existing approaches do not learn from the data and require the definition of queries and rules for describing each behavior. In this paper, we discuss challenges and opportunities in classical machine learning and deep learning for vessel event and anomaly detection. We hope to motivate the research of novel methods and tools, since addressing these challenges is an essential step towards actual intelligent maritime monitoring systems.

With my ongoing goal to be the Purchaser of the Future, and thanks to my current studies on Machine Learning, a new world has opened-up to me, and I can tell you, it's been very addictive. In the beginning of 2019, apart from the autonomous vehicles and the autonomous ship project from Kongsberg, I had no idea about what was really going on the field of #artificialintelligence. Checking here and there, I decided to dig into it, so I shared 10 Amazing Projects using DIGITALISATION within Maritime. So many nice projects I have discovered last month! Really amazing to see these fantastic ideas being transformed into algorithms, using all this DATA to improve performance, decision taking and with an inimaginable speed. Either on Maritime or in the Oil&Gas, here are some of the companies that really impressed me.

The innovative reapplication of a multiagent system for human-in-the-loop (HIL) simulation was a consequence of appropriate agent-oriented design. The use of intelligent agents for simulating human decision making offers the potential for analysis and design methodologies that do not distinguish between agent and human until implementation. With this as a driver in the design process, the construction of systems in which humans and agents can be interchanged is simplified. Two systems have been constructed and deployed to provide defense analysts with the tools required to advise and assist the Australian Defense Force in the conduct of maritime surveillance and patrol. The experiences gained from this process indicate that it is simpler, both in design and implementation, to add humans to a system designed for intelligent agents than it is to add intelligent agents to a system designed for humans.

Previously, I looked at how Edinburgh's universities were leading the city's robotics and AI efforts, and in the process turning it into a destination for research and education in Europe. In this article I will be looking at the businesses side of the Scottish robotics landscape, and the ways that Scotland is embracing robotics and AI as an industry. One key area that Scottish robotics are thriving in is the maritime industry, specifically the development of autonomous underwater vehicles (AUVs). Scotland has a rich maritime history, and with the rise of a maritime robotics sector in Scotland, it looks like this tradition is still strong. In the manufacturing sector, Scottish companies are also increasingly eager to embrace robotics and automation, primarily as a way to cut costs, improve competitiveness, innovate new products and expand their global market share.