Self-supervised vessel trajectory segmentation via learning spatio-temporal semantics

The study of vessel trajectories (VTs) holds significant benefits for marine route management and resource development. VT segmentation serves as a foundation for extracting vessel motion primitives and enables analysis of vessel manoeuvring habits and behavioural intentions. However, existing methods relying on predefined behaviour patterns face high labelling costs, which hinder accurate pattern recognition. This paper proposes a self-supervised vessel trajectory segmentation method (SS-VTS), which segments VTs based on their inherent spatio-temporal semantics. SS-VTS adaptively divides VTs into cells of optimal size. Then, it extracts split points on different semantic levels from the multi-dimensional feature sequence of the VTs using self-supervised learning. Finally, spatio-temporal distance fusion module is performed on split points to determine change points and obtain VT segments with multiple semantics. Experiments on a real automatic identification system datasets show that SS-VTS achieves state-of-the-art segmentation results compared to seven baseline methods. This article introduces a self-supervised vessel trajectory segmentation method for automatically analysing vessel motion. The proposed method dynamically divides vessel trajectories into cells of optimal size and identifies split points based on inherent spatiotemporal semantics using self-supervised learning. The results, evaluated on a real automatic identification system dataset, demonstrate that self-supervised vessel trajectory segmentation method outperforms seven baseline methods, providing state-of-the-art segmentation for studying vessel manoeuvring habits and behavioural intentions. image