NCL-DASB: GEO-Located Maritime Surveillance Labeled Dataset and Annotation API

Due to maritime transportation being the most crucial mode in international trade, maritime traffic safety significantly influences global economic development. Detecting anomalous ship behaviors (DASB) serves as a critical measure to safeguard maritime traffic safety. In recent years, data-driven deep learning technologies have witnessed remarkable advancements, and the introduction of high-quality DASB datasets facilitates the rapid and effective transformation of traditional DASB methods into intelligent ones. In this paper, we initially present a labeled DASB dataset named NCL-DASB, recorded at the Tynemouth port in Newcastle, UK. Subsequently, we propose a standard framework for processing vessel AIS data, enabling the transformation of AIS data into vessel trajectory feature information suitable for deep learning through preprocessing. Finally, we open-source an API for annotating vessel trajectory data in the NCL-DASB dataset, intended for the use of future researchers in their studies.