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

Leiyu Xie, Federico Angelini, Syed Mohsen Naqvi

Abstract: 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.