Maritime Situational Awareness (MSA) is crucial for identifying suspicious vessel activities, such as dark-ship operations and prolonged loitering activities. However, the development of robust detection systems requires high-quality datasets that capture vessel encounters, particularly encounters that occur beyond 20 nautical miles (NM) from the coast. This paper presents the creation and analysis of a comprehensive data set that contains vessel trajectories associated with offshore encounters. The dataset, constructed using 12 months of data from the Marine Cadastre Automatic Identification System (AIS), leverages the H3 geohash system for spatial proximity detection and MovingPandas for trajectory extraction. The dataset analysis demonstrates that the dataset is a powerful tool for enhancing Maritime Domain Awareness (MDA), contributing to monitoring and security in the maritime environment. The analysis of encounter patterns highlights both the importance of reliable data and the need for a robust detection system to address uncertainties and information gaps.

Detecting fishing trajectories in maritime surveillance is of the utmost importance for identifying illegal fishing activity. In the event of illegal fishing activity, the maritime authority can mobilize resources to engage the vessel; hence, a false flag can be costly. This study investigates the efficacy of ensemble learning techniques for boosting individual model performance and decreasing uncertainty. Employing a range of machine learning models, including logistic regression, decision trees, random forests, neural networks, gradient boosting, and recurrent neural networks, the research evaluates the combination of these using ensemble methods like ensemble mean, weighted ensemble, and stacking approaches to enhance precision and decrease uncertainty. The primary dataset comprises a combination of fishing vessel and cargo vessel trajectories to train and test the models. Methodologically, the paper details the process of data analysis and the application of ensemble learning. A comparative assessment of individual models versus ensemble techniques forms the crux of this study. Results indicate a marked improvement in accuracy and consistency when employing ensemble methods, with weighted and stacking ensembles showing particular promise. These findings suggest that ensemble models outperform their individual counterparts in the context of maritime surveillance. This research makes a notable contribution to the maritime surveillance domain, demonstrating the potential of ensemble learning in enhancing detection capabilities for illegal fishing activities. The implications of these advancements are critical for maritime authorities as they strive to effectively monitor and protect marine ecosystems.