Architecture for Trajectory-Based Fishing Ship Classification with AIS Data

TL;DR

This paper presents a data preparation and classification approach for identifying fishing vessels from AIS trajectory data, addressing real-world data issues and class imbalance, with promising experimental results.

Contribution

It introduces a novel data preparation process and feature extraction method for classifying fishing ships using AIS data, handling noise and class imbalance effectively.

Findings

Effective data preparation improves classification accuracy.

Minimal AIS information suffices for reliable classification.

Proposed features generalize beyond fishing vessel detection.

Abstract

This paper proposes a data preparation process for managing real-world kinematic data and detecting fishing vessels. The solution is a binary classification that classifies ship trajectories into either fishing or non-fishing ships. The data used are characterized by the typical problems found in classic data mining applications using real-world data, such as noise and inconsistencies. The two classes are also clearly unbalanced in the data, a problem which is addressed using algorithms that resample the instances. For classification, a series of features are extracted from spatiotemporal data that represent the trajectories of the ships, available from sequences of Automatic Identification System (AIS) reports. These features are proposed for the modelling of ship behavior but, because they do not contain context-related information, the classification can be applied in other scenarios. Experimentation shows that the proposed data preparation process is useful for the presented classification problem. In addition, positive results are obtained using minimal information.