Maritime Security with Geospatial Integration For Indonesia’s Archipelagic Sea Lanes (ALKI)

Auriga Putri Faradilla; Rudiyanto; Widodo

doi:10.63922/ijevss.v4i04.2302

Auriga Putri Faradilla Indonesia Defense University, Indonesia
Rudiyanto Indonesia Defense University, Indonesia
Widodo Indonesia Defense University, Indonesia

DOI: https://doi.org/10.63922/ijevss.v4i04.2302

Keywords: Maritime Security, Geospatial Intelligence, Archipelagic Sea Lanes (ALKI), Maritime Surveillance, Automatic Identification System (AIS), Remote Sensing

Abstract

Advances in digital technology have revolutionized the maritime sector through the use of Automatic Identification Systems (AIS), artificial intelligence (AI), remote sensing, and geospatial analytics, which are increasingly important in supporting Maritime Domain Awareness (MDA). Various studies show that AIS now functions as a strategic data source for predictive analytics, risk mapping, and even detecting anomalous vessel behavior, while big data and AI drive energy efficiency, logistics optimization, and intelligent navigation systems. Various methodological innovations can enable the integration of heterogeneous data to produce more accurate ship accident risk maps, while the combination of AIS with satellite imagery strengthens the detection of illegal activities such as IUU fishing in remote areas. Furthermore, the application of machine learning and deep learning algorithms has proven effective in detecting data manipulation and abnormal navigation patterns, thus strengthening early detection and rapid response systems. For Indonesia, which has strategic shipping lanes, the integration of these technologies is highly relevant to strengthen maritime surveillance, enhance maritime security, and maintain sovereignty amidst the region's geopolitical dynamics.

References

Li, H., Xujie, R., & Zaili, Y. (2023). Data-driven Bayesian network for risk analysis of global maritime accidents. Reliability Engineering and System Safety, 230. https://doi.org/10.1016/j.ress.2022.108938.
Meyers, S. D., Laura, A., & Mark, E. L. (2021). A Scopus-based bibliometric study of maritime research involving the Automatic Identification System. Transportation Research Interdisciplinary Perspectives, https://doi.org/10.1016/j.trip.2021.100387.
Munim, Z. H., Mariia, D., Veronica, J. J., Mohammad, H. S., & Marius, I. (2020). Big data and artificial intelligence in the maritime industry: a bibliometric review and future research directions. Maritime Policy & Management, 47(5), 577-597. DOI: 10.1080/03088839.2020.1788731.
Rawson, A., Zoheir, S., & Mario, B. (2022). Intelligent geospatial maritime risk analytics using the Discrete Global Grid System. Big Earth Data, 6(3), 294-322. https://doi.org/10.1080/20964471.2021.1965370.
Schofield, C. (2021). Geographical Dimensions to Global Oceans Governance. Geographical Review, 113(1), 20-47, DOI: 10.1080/00167428.2020.1852879.
That, V. V., Le. T. L., & Ta, Q. T. (2023). Maritime security policy of India in early 21st century: Vietnam’s perceptive of its implication on the Asia-Pacific region. Journal of International Studies, 19(2), 67-92. https://doi.org/10.32890/jis2023.19.2.3
Wolsing, K., Linus, R., Jan, B., & Klaus, W. (2022). Anomaly Detection in Maritime AIS Tracks: A Review of Recent Approaches. Journal of Marine Science and Engineering, 10, 112. https:// doi.org/10.3390/jmse10010112.