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Related Experiment Video

Updated: Dec 15, 2025

Using an Automated 3D-tracking System to Record Individual and Shoals of Adult Zebrafish

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Architecture for Trajectory-Based Fishing Ship Classification with AIS Data.

David Sánchez Pedroche¹, Daniel Amigo¹, Jesús García¹

¹Group GIAA, University Carlos III of Madrid, 28270 Madrid, Spain.

Sensors (Basel, Switzerland)

|July 10, 2020

Summary

This summary is machine-generated.

This study introduces a data preparation method for real-world kinematic data to detect fishing vessels using ship trajectory classification. The approach effectively handles noisy, inconsistent, and imbalanced data for improved accuracy.

Keywords:

AIS data class imbalance data fusion machine learning real-world data spatiotemporal data mining trajectory classification

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Area of Science:

Data Science
Maritime Analytics
Machine Learning

Background:

Real-world kinematic data often presents challenges like noise and inconsistencies.
Classifying ship trajectories, particularly distinguishing fishing vessels, requires robust data handling.

Purpose of the Study:

To propose a data preparation process for managing real-world kinematic data.
To develop a binary classification model for identifying fishing vessels based on ship trajectories.

Main Methods:

Feature extraction from spatiotemporal data derived from Automatic Identification System (AIS) reports.
Application of resampling algorithms to address class imbalance in the dataset.
Binary classification to categorize ship trajectories as fishing or non-fishing.

Main Results:

The proposed data preparation process demonstrates utility in the classification task.
Effective handling of noisy, inconsistent, and imbalanced datasets was achieved.
Positive classification results were obtained with minimal contextual information.

Conclusions:

The developed data preparation strategy is effective for kinematic data analysis.
The method shows promise for identifying fishing vessels and can be adapted to other classification scenarios.
Minimal information is sufficient for successful ship behavior modeling and classification.