Main Article Content
Abstract
The safety of shipping and the resilience of maritime infrastructure are affected by the accuracy of the early warning system against extreme weather. The use of Geospatial Intelligence (GEOINT) acts as an integrative framework that combines remote sensing data, spatial analysis, and the interpretation of geographic information for the increasing need for technology-based early detection in the face of increasingly complex marine climate dynamics, both for civilian and national defense interests. The purpose of this research is to analyze the development of global research over the past 25 years on this topic, as well as relate it to maritime infrastructure defense and resilience policies. The methodology used is bibliometric with a quantitative analysis approach to Scopus and Google Scholar publication data, as well as visualization using VOSviewer. The analysis includes the mapping of keyword trends, thematic clusters, institutional actors, and the evolution of research related to GEOINT, remote sensing, and maritime early warning systems. The results show significant improvements in the topic of satellite utilization (MODIS, Sentinel) and the integration of GEOINT big data and spatial analytics for early warning systems, but research on its application in the context of maritime defense policy is still limited. These findings provide strategic direction for the development of GEOINT as a data-driven policy support instrument that supports national shipping resilience and military preparedness in strategic maritime areas. The study also recommends a cross-sectoral research agenda that is more adaptive to the threat of extreme marine weather.
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Copyright (c) 2026 Fitri Anggraeni Sekar Dwianti, Trismadi, Syachrul Arief, Asep Adang Supriyadi

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This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
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References
Al Husaeni, D. F., & Nandiyanto, A. B. D. (2022). Bibliometric Using Vosviewer with Publish or Perish (using Google Scholar data): From Step-by-step Processing for Users to the Practical Examples in the Analysis of Digital Learning Articles in Pre and Post Covid-19 Pandemic. ASEAN Journal of Science and Engineering, 2(1), 19–46. https://doi.org/10.17509/ajse.v2i1.37368
Asadabadi, A., & Hooks, E. M. (2020). Maritime Port Network Resiliency and Reliability Through co-opetition. Transportation Research Part E: Logistics and Transportation Review, 137:101916.https://doi.org/10.1016/j.tre.2020.101916
Chen, L., Xia, C., Zhao, Z., Fu, H., & Chen, Y. (2024). AI-Driven Sensing Technology : Review.
Cong, Y., Gu, C., Zhang, T., & Gao, Y. (2021). Underwater robot sensing technology: A survey. Fundamental Research, 1(3), 337–345. https://doi.org/10.1016/j.fmre.2021.03.002
Coughlan de Perez, E., Berse, K. B., Depante, L. A. C., Easton-Calabria, E., Evidente, E. P. R., Ezike, T., Heinrich, D., Jack, C., Lagmay, A. M. F. A., Lendelvo, S., Marunye, J., Maxwell, D. G., Murshed, S. B., Orach, C. G., Pinto, M., Poole, L. B., Rathod, K., Shampa, & Van Sant, C. (2022). Learning from the past in moving to the future: Invest in communication and response to weather early warnings to reduce death and damage. Climate Risk Management, 38(August), 100461. https://doi.org/10.1016/j.crm.2022.100461
Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133:285-296. https://doi.org/10.1016/j.jbusres.2021.04.070
Goksu, I. (2021). Bibliometric mapping of mobile learning. Telematics and Informatics. https://doi.org/10.1016/j.tele.2020.101491
Hossain, N. U. I., El Amrani, S., Jaradat, R., Marufuzzaman, M., Buchanan, R., Rinaudo, C., & Hamilton, M. (2020). Modelling and Assessing Interdependencies between Critical Infrastructures using Bayesian Network: A Case Study of Inland Waterway Port and Surrounding Supply Chain Network. Reliability Engineering & System Safety, 98:106898. https://doi.org/10.1016/j.ress.2020.106898
Jing, Y., Wang, C., Chen, Y., Wang, H., Yu, T., & Shadiev, R. (2023). Bibliometric mapping techniques in educational technology research: A systematic literature review. Education and Information Technologies. https://doi.org/10.1007/s10639-023-12178-6
Kim, D. H. (2014). A Study on the Defense Geospatial Intelligence Governance - Focusing on the Intelligence Community and LandWarNet. Journal of Korea Spatial Information Society, 22(1), 19–26. https://doi.org/10.12672/ksis.2014.22.1.019
Li, G., Zhang, X., Jiang, L., Wang, C., Huang, R., & Liu, Z. (2024). An approach for traffic pattern recognition integration of ship AIS data and port geospatial features. Geo-Spatial Information Science, 27(6), 2048–2075. https://doi.org/10.1080/10095020.2024.2308715
Liu, Y., Wang, S., Xie, Y., Xiong, T., & Wu, M. (2024). A Review of Sensing Technologies for Indoor Autonomous Mobile Robots. Sensors, 24, 1222. https://doi.org/10.3390/s24041222
Pessin, V. Z., Santos, C. A. S., Yamane, L. H., Siman, R. R., Baldam, R. de L., & Júnior, V. L. (2023). A method of Mapping Process for scientific production using the Smart Bibliometrics. MethodsX, 11(February), 102367. https://doi.org/10.1016/j.mex.2023.102367
Pessin, V. Z., Yamane, L. H., & Siman, R. R. (2022). Smart bibliometrics: an integrated method of science mapping and bibliometric analysis. Scientometrics, 127(6), 3695–3718. https://doi.org/10.1007/s11192-022-04406-6
Prieto-Jiménez, E., López-Catalán, L., López-Catalán, B., & Domínguez-Fernández, G. (2021). Sustainable development goals and education: A bibliometric mapping analysis. Sustainability (Switzerland), 13(4), 1–20. https://doi.org/10.3390/su13042126
Rahmandhala, I. D., Supriyadi, A. A., & Prihanto, Y. (2024). Geospatial Intelligent Analysis to Support Indonesian Airspace Defense. 3(9), 2149–2168.
Raju, C. M., Elpa, D. P., & Urban, P. L. (2024). Automation and Computerization of (Bio)sensing Systems. ACS Sensors, 9(3), 1033–1048. https://doi.org/10.1021/acssensors.3c01887
Spencer, B. F. (2003). Opportunities and challenges for smart sensing technology. Structural Health Monitoring and Intelligent Infrastructure - Proceedings of the 1st International Conference on Structural Health Monitoring and Intelligent Infrastructure, 1(180), 65–71.
Sun, K., Cui, W., & Chen, C. (2021). Review of underwater sensing technologies and applications. Sensors, 21(23), 1–28. https://doi.org/10.3390/s21237849
Turner, I. L., Harley, M. D., Almar, R., & Bergsma, E. W. J. (2021). Satellite optical imagery in Coastal Engineering. Coastal Engineering, 167, 103919. https://doi.org/10.1016/j.coastaleng.2021.103919
Van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84:523-538. https://doi.org/10.1007/s11192-009-0146-3
Viaña-Borja, S. P., González-Villanueva, R., Alejo, I., Stumpf, R. P., Navarro, G., & Caballero, I. (2025). Satellite-derived bathymetry using Sentinel-2 in mesotidal coasts. Coastal Engineering, 195(June 2024). https://doi.org/10.1016/j.coastaleng.2024.104644
Yaddanapudi, R., Mishra, A., Huang, W., & Chowdhary, H. (2022). Compound Wind and Precipitation Extremes in Global Coastal Regions Under Climate Change. Geophysical Research Letters, 49(15). https://doi.org/10.1029/2022GL098974
Yang, D., Wu, L., Wang, S., Jia, H., & Li, K. X. (2019). How big data enriches maritime research. 2019, 1–22. https://doi.org/10.1177/10944281145626
Zupic, I., & Cater, T. (2014). Bibliometric Methods in Management and Organization. Organizational Research Methods, 18:3. https://doi.org/10.1177/10944281145626
