AIS-Based Vessel Trajectory Reconstruction with U-Net Convolutional Networks

The vessel trajectory data indicated by the Automatic Identification System (AIS) is important and useful in maritime data analysis, navigational safety and maritime risk assessment. However, the raw trajectory data contains noise, missing data and other errors which can lead to a wrong conclusion. Therefore, it is essential to develop a vessel trajectory reconstruction method, which is meaningful for enhancing the applicability of vessel trajectory and improving the navigation safety. In recent years, there have been many studies about vessel trajectory reconstruction, but the performance of these methods will degrade when they are faced with curved trajectories with high loss rate. In this paper, we propose a novel trajectory reconstruction method via U-net. Benefiting from the architecture of U-net, this method makes great use of historical trajectories and takes advantage of the rich skip connections in this network which help copy low-level features to corresponding high-level features. Consequently, this method is robust to the trajectories with different sampling rates, missing points, and noisy data. In addition, the proposed method is tested and compared with cubic spline interpolation. The results show that our method is capable of higher accuracy than the cubic spline interpolation especially when the trajectories are curved and have a high loss rate.