Semi-Supervised Learning-Based AIS Stop Pattern Analysis and Detection of Unofficial Anchorage Areas

For efficient port logistics and precise maritime traffic management, automatic recognition of vessel anchoring behavior is essential. This study proposes a semi-supervised learning–based anchorage detection framework using AIS data to overcome the limitations of official anchorage information. By quantifying vessel stopping trajectory features and applying a stepwise semi-supervised structure based on high-confidence data, the method reduces labeling costs while maintaining generalization across ports. Trained with Busan and Ulsan data and validated with Yeosu–Gwangyang and Pyeongtaek–Dangjin data, the model achieved 98% accuracy and an F1-score of 0.98. The results demonstrate the potential of Automatic Identification System (AIS)-based automation for port activity recognition and its applicability to port operation optimization and real-time vessel monitoring.