Improved AIS data simplification algorithm for extracting typical routes considering motion continuity
Jin He, Jinjia Ruan, Yao Tong, Guojin Qin, Guojin Qin, Guojin Qin
TL;DR
This paper introduces an improved algorithm for simplifying AIS data to extract typical vessel routes while maintaining motion continuity, enhancing deep learning applications in maritime monitoring.
Contribution
The novel contribution is an enhanced distance threshold pruning technique that preserves vessel movement continuity in AIS data simplification.
Findings
Simplified data improves training efficiency and prediction accuracy in trajectory forecasting models.
Anomaly detection capabilities are enhanced with fewer false positives using the simplified data.
The method achieves faster convergence in deep learning models compared to using raw AIS data.
Abstract
Automatic Identification System (AIS) data provides crucial information about vessel trajectories. However, raw AIS data is often highly redundant, containing overlapping and repetitive routes, which complicates its direct use in maritime applications such as navigation planning and route prediction. In this paper, we propose an improved simplification algorithm designed to extract typical routes while preserving vessel movement continuity. Our approach simplifies complex AIS data by applying an enhanced distance threshold pruning technique and analyzing the continuity of vessel operations to address route segment discontinuities and coordinate deviations.We conducted experiments to evaluate the impact of the simplification algorithm on deep learning applications, specifically in trajectory prediction and anomaly detection. The results demonstrate that the simplified data significantly…
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Taxonomy
TopicsMaritime Navigation and Safety · Anomaly Detection Techniques and Applications · Historical Geography and Cartography