Adopting machine learning solutions for monitoring vessel behaviour and surveillance in the maritime domain shows excellent promise. However, significant challenges arise due to the lack of publicly available vessel trajectory data labelled with Automatic Identification System (AIS) information. A new automated system has been proposed to preprocess and label vessel trajectory data collected from AIS at the Port of New York (NY) USA, Blyth Port in Newcastle (NCL), United Kingdom, and a combined dataset called NYCL to address the labelling problem. This automated labelling system functions in three key stages. The first stage involves data collection and processing. The second stage transforms raw AIS data into meaningful vessel trajectory information. The third stage annotates and labels these trajectories, concluding with classification. The processed AIS data create vessel trajectories, with labels automatically generated. Finally, this work explores the classification models to demonstrate the effectiveness of labelled vessel trajectories in various maritime tasks.