Vessel Trajectory Reconstruction Of Missing AIS Data Values Machine Learning.

The report discusses the importance of Automatic Identification System (AIS) data in tracking vessel trajectories and its applications in various fields such as mapping shipping density, characterizing marine traffic patterns, anomalous behavior detection of ships, collision risk analysis, investigation of maritime accidents, and maritime route generation for vessels. The report also highlights the challenges of working with AIS data, which is often plagued with inconsistencies and noise. Additionally, it provides an overview of several works on trajectory quality improvement, including methods such as Piecewise Linear Interpolation, Piecewise Cubic Interpolation, cubic Hermit interpolation, and the discrete Kalman algorithm. The paper concludes by suggesting future research directions in the field of AIS data analysis.

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1. AIS is an automatic vessel tracking system that broadcasts important messages describing the vessel and its sailing status information

2. Ship trajectories have various critical applications, including mapping shipping density, characterizing marine traffic patterns, detecting anomalous behaviour of ships, collision risk analysis, investigating maritime accidents, and generating maritime routes for vessels.

3. Notable works have been done for each application, such as visualizing ship routes, deriving shipping density maps, detecting and mapping fishing activities, and developing anomaly detectors for detecting anomalous vessel behaviour.

4. Trajectory quality improvement has been addressed by various approaches such as Piecewise Linear/Cubic/Spline Interpolation, trajectory restoration based on navigational features of the vessel, linear interpolation, cubic Hermit interpolation, and discrete Kalman algorithm.

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