City Research Online

Abstract

The development and evaluation of autonomous maritime vessels rely heavily on data-driven insights from iterative testing and analysis. While initial analyses are often conducted on small experimental datasets to explore key system characteristics, scaling these analyses to large datasets presents significant challenges. In this study, we extend our prior work on visual exploration of small-scale test bed data by proposing approaches to scaling the visual analytics techniques to large datasets. Using AIS data from ferry boats as a proxy for extensive maritime drone operations, we address the challenges of large-scale data exploration over eight days of repetitive ferry movements across a busy strait, simulating conditions suitable for autonomous vessels. Our approach investigates movement patterns, operational stability during repeated trips, and potential collision scenarios. To support such analyses, we propose a general, reusable workflow and a set of practical guidelines for applying visual analytics techniques to large maritime movement datasets. The findings highlight the scalability and adaptability of visual analytics methods, providing valuable tools for analyzing complex maritime datasets and advancing autonomous vessel technologies.

Publication Type:	Article
Additional Information:	This is an open access article distributed under the terms of the Creative Commons CC-BY license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Publisher Keywords:	Visual analytics, Trajectories, Vessel movement
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments:	School of Science & Technology School of Science & Technology > Department of Computer Science
SWORD Depositor:	Symplectic Administrator

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