Recent advancements in sensor and tracking technologies have facilitated the real-time tracking of marine vessels as they traverse the oceans. As a result, there is an increasing demand to analyze these datasets to derive insights into vessel movement patterns and to investigate activities occurring within specific spatial and temporal contexts. This survey offers a comprehensive review of contemporary research in trajectory data mining, with a particular focus on maritime applications. The article collects and evaluates state-of-the-art algorithmic approaches and key techniques pertinent to various use case scenarios within this domain. Furthermore, this study provides an in-depth analysis of recent developments in trajectory data mining as applied to the maritime sector, identifying available data sources and conducting a detailed examination of significant applications, including trajectory forecasting, activity recognition, and trajectory clustering.

The task of accurately forecasting the trajectory of a vessel, and in general a moving object operating in free space until its destination remains an open challenge. This paper addresses this problem by describing an unsupervised data-driven framework for short and extended horizon forecasts, from the perspectives of data mining and machine learning. We propose a data-driven algorithmic approach named “EnvClus*’ that models efficiently historical vessel trajectories at a global scale, forming a mobility graph that depicts the most likely movements among two ports. EnvClus* is able to make tailored route forecasts considering the characteristics of the vessels (i.e. length, draught) along with information regarding the executed trip. The proposed method is able to forecast the most likely realistic and smooth trajectory from a given query position of a vessel (entire route or underway forecasting) towards its destination port. We illustrate the accuracy and effectiveness of our method through a series of scenarios for long and short term forecasting using real world data from around the globe. These experiments indicate an overall improvement of 33% over state-of-the-art and baseline methods; with the benefits of our approach being more apparent when dealing with longer trips from container vessels.

Density maps support a bird’s eye view of vessel traffic, through providing an overview of vessel behavior, either at a regional or global scale in a given timeframe. However, any inaccuracies in the underlying data, due to sensor noise or other factors, evidently lead to erroneous interpretations and misleading visualizations. In this work, we propose a novel algorithmic framework for generating highly accurate density maps of shipping activities, from incomplete data collected by the Automatic Identification System (AIS). The complete framework involves a number of computational steps for (1) cleaning and filtering AIS data, (2) improving the quality of the input dataset (through trajectory reconstruction and satellite image analysis) and (3) computing and visualizing the subsequent vessel traffic as density maps. The framework describes an end-to-end implementation pipeline for a real world system, capable of addressing several of the underlying issues of AIS datasets. Real-world data are used to demonstrate the effectiveness of our framework. These experiments show that our trajectory reconstruction method results in significant improvements up to 15% and 26% for temporal gaps of 3–6 and 6–24 h, respectively, in comparison to the baseline methodology. Additionally, a use case in European waters highlights our capability of detecting “dark vessels”, i.e., vessel positions not present in the AIS data.

The “Aegean Ro-boat Race” is an international university level competition, challenging teams to design and develop innovative autonomous robotic systems that can perform at sea in real world conditions. This is an innovation race amongst universities, but with the support of industry, that merges visionary thinking from different fields to solve problems in marine robotics and autonomous systems. Topics to be judged include autonomous behavior, sensor fusion, object detection, path planning, reliability, and overall innovation. This year's competition took place in Syros, Greece on the 12th of July 2023, and consisted of three mission tasks, focused on i) high speed and performance ii) collision avoidance and iii) endurance. This work details what happened during the first “Aegean Ro-boat Race”, while presenting the results of this unique learning experience and discussing advances achieved in terms of innovation.