Big Data-Based Literature Study on Automatic Identifition System Data and Synthetic Aparture Radar Image Integration for Illegal Fishing in Maritime Awareness

Department of Naval Architecture, Institut Teknologi Sepuluh Nopember, Surabaya, East Java, Indonesia

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Abstract

Illegal, unreported, and unregulated (IUU) fishing remains a persistent threat in Indonesian waters, causing substantial economic losses and long-term ecological damage. This review synthesizes methods for fusing Automatic Identification System (AIS) data with Synthetic Aperture Radar (SAR) imagery to enhance maritime surveillance. AIS conveys vessel identity and reported position, whereas SAR detects vessels operating without AIS (“dark” vessels). The review covers approaches to spatiotemporal synchronization, data association, and machine-learning models that jointly exploit both modalities. In addition, this study provides a systematic mapping of recent AIS–SAR fusion methods and proposes a conceptual big data framework tailored to Indonesia’s maritime surveillance context. According to the surveyed literature, AIS–SAR fusion has been reported to improve the identification of non-cooperative vessels, reduce false alarm and missed detection rates, and shorten response times. Effective implementation requires reliable spatiotemporal alignment, adequate computing resources for large-scale processing, and interagency data-sharing mechanisms. Collectively, the evidence indicates that large-scale AIS–SAR fusion can enhance maritime awareness and support Indonesia’s efforts to counter IUU fishing.