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Automatic Ship Identification Systems (AIS) play a key role in monitoring maritime traffic, providing the data necessary for analysis and decision-making. The integrity of this data is fundamental to the correctness of inference and decision-making in the context of maritime safety, traffic management and environmental protection. This paper analyzes the impact of data integrity in large AIS datasets, on classification accuracy. It also presents error detection and correction methods and data verification techniques that can improve the reliability of AIS systems. The results show that improving the integrity of AIS data significantly improves the quality of inference, which has a direct impact on operational efficiency and safety at sea.

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Data Integrity Versus Inference Accuracy in Large AIS Datasets

  • 章节

  • 发表日期：

    2025年

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  • Adam Kiersztyn,
  • Dariusz Czerwinski,
  • Aneta Oniszczuk-Jastrzabek,
  • Ernest Czermanski,
  • Agnieszka Rzepka

摘要

Automatic Ship Identification Systems (AIS) play a key role in monitoring maritime traffic, providing the data necessary for analysis and decision-making. The integrity of this data is fundamental to the correctness of inference and decision-making in the context of maritime safety, traffic management and environmental protection. This paper analyzes the impact of data integrity in large AIS datasets, on classification accuracy. It also presents error detection and correction methods and data verification techniques that can improve the reliability of AIS systems. The results show that improving the integrity of AIS data significantly improves the quality of inference, which has a direct impact on operational efficiency and safety at sea.