Architecture for Trajectory-Based Fishing Ship Classification with AIS Data

Clicks: 326
ID: 266586
2020
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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.
Reference Key
pedroche2020sensorsarchitecture Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors David Sánchez Pedroche;Daniel Amigo;Jesús García;José Manuel Molina;Sánchez Pedroche, David;Amigo, Daniel;García, Jesús;Molina, José Manuel;
Journal sensors
Year 2020
DOI
10.3390/s20133782
URL
https://www.mdpi.com/1424-8220/20/13/3782
Keywords
Machine learning real-world data class imbalance data fusion ais data spatiotemporal data mining trajectory classification

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