Remote Sensing Big Data Analysis to Enhance Strategic Intelligence Capability in National Security

Authors

  • Hanny Maula Yasien Sekolah Tinggi Intelijen Negara
  • I Gusti Ngurah Bagus Sucitra Sekolah Tinggi Intelijen Negara
  • Moch. Djatmiko Bhayangkara University

DOI:

https://doi.org/10.70710/sitj.v3i3.110

Keywords:

Artificial Intelligence, Big Data Analytics, Geospatial Intelligence, National Security, Remote Sensing, Strategic Intelligence

Abstract

The increasing complexity of national security threats in the digital era requires intelligence systems capable of conducting rapid, precise, and predictive analysis. This paper examines how remote sensing big data analysis can enhance strategic intelligence capability in national security by integrating satellite imagery, geospatial data, artificial intelligence, machine learning, deep learning, and cybersecurity mechanisms. Using a qualitative literature review approach, this study analyzes secondary sources from peer-reviewed journals, cybersecurity studies, government-related reports, and technology documents relevant to remote sensing and big data analytics. The findings show that remote sensing big data strengthens strategic intelligence by improving situational awareness, supporting early warning, detecting military and environmental changes, monitoring critical infrastructure, and enabling faster strategic decision-making. However, the effectiveness of this technology depends on data security, system reliability, inter-agency coordination, and the integration of cybersecurity mechanisms such as intrusion detection systems, federated learning, blockchain, and secure data governance. Therefore, remote sensing big data should be developed as an integrated intelligence ecosystem that combines advanced geospatial analytics, real-time data processing, cyber resilience, and institutional coordination to support national security.

Downloads

Download data is not yet available.

References

Albakri, A., Alabdullah, B., & Alhayan, F. (2023). Blockchain-assisted machine learning with hybrid metaheuristics-empowered cyber attack detection and classification model. Sustainability, 15(18), 13887. https://doi.org/10.3390/su151813887

Bukhari, S. M. S., Zafar, M. H., Houran, M. A., Moosavi, S. K. R., Mansoor, M., Muaaz, M., & Sanfilippo, F. (2024). Secure and privacy-preserving intrusion detection in wireless sensor networks: Federated learning with SCNN-Bi-LSTM for enhanced reliability. Ad Hoc Networks, 155, 103407. https://doi.org/10.1016/j.adhoc.2024.103407

Chebbi, I., Mellouli, N., Farah, I. R., & Lamolle, M. (2021). Big remote sensing image classification based on deep learning extraction features and distributed Spark frameworks. Big Data and Cognitive Computing, 5(2), 21. https://doi.org/10.3390/bdcc5020021

Chierici, A., Malizia, A., Di Giovanni, D., Ciolini, R., & D’Errico, F. (2022). A high-performance gamma spectrometer for unmanned systems based on off-the-shelf components. Sensors, 22(3), 1078. https://doi.org/10.3390/s22031078

Das, A. (2025). Surface water quality evaluation of Mahanadi and its tributary Katha Jodi River, Cuttack District, Odisha, using WQI, PLSR, SRI, and geospatial techniques. Applied Water Science, 15(2). https://doi.org/10.1007/s13201-024-02357-2

Elali, F. R., & Rachid, L. N. (2023). AI-generated research paper fabrication and plagiarism in the scientific community. Patterns, 4(3), 100706. https://doi.org/10.1016/j.patter.2023.100706

Fan, F., Shi, Y., Guggemos, T., & Zhu, X. X. (2023). Hybrid quantum-classical convolutional neural network model for image classification. IEEE Transactions on Neural Networks and Learning Systems, 35(12). https://doi.org/10.1109/TNNLS.2023.3312170

Feng, Q., Chen, B., Li, G., Yao, X., Gao, B., & Zhang, L. (2022). A review for sample datasets of remote sensing imagery. National Remote Sensing Bulletin, 26(4), 589–605. https://doi.org/10.11834/jrs.20221162

Gheyas, I. A., & Abdallah, A. E. (2016). Detection and prediction of insider threats to cyber security: A systematic literature review and meta-analysis. Big Data Analytics, 1(6). https://doi.org/10.1186/s41044-016-0006-0

Guo, J., Huang, C., & Hou, J. (2022). A scalable computing resources system for remote sensing big data processing using GeoPySpark based on Spark on K8s. Remote Sensing, 14(3), 521. https://doi.org/10.3390/rs14030521

Jia, B., Cheng, Z., Wang, C., Zhao, J., & An, N. (2023). CA-BIT: A change detection method of land use in natural reserves. Agronomy, 13(3), 635. https://doi.org/10.3390/agronomy13030635

Kwan, C. (2018). Remote sensing performance enhancement in hyperspectral images. Sensors, 18(11), 3598. https://doi.org/10.3390/s18113598

Ma, X., Liu, H., & Peng, Z. (2023). Assimilating a blended dataset of satellite-based estimations and in situ observations to improve WRF-Chem PM2.5 prediction. Atmospheric Environment, 319, 120284. https://doi.org/10.1016/j.atmosenv.2023.120284

Mohan, A., Meskin, N., & Mehrjerdi, H. (2020). A comprehensive review of the cyber-attacks and cyber-security on load frequency control of power systems. Energies, 13(15), 3860. https://doi.org/10.3390/en13153860

Nes, K., Schaefer, A., Gammans, M., & Scheitrum, D. P. (2025). Extreme weather events, climate expectations, and agricultural export dynamics. American Journal of Agricultural Economics, 107(3), 826–845. https://doi.org/10.1111/ajae.12505

Partheepan, S., Sanati, F., & Hassan, J. (2023). Autonomous unmanned aerial vehicles in bushfire management: Challenges and opportunities. Drones, 7(1), 47. https://doi.org/10.3390/drones7010047

Sarretta, A., Napolitano, M., & Minghini, M. (2023). OpenStreetMap as an input source for producing governmental datasets: The case of the Italian Military Geographic Institute. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLVIII-4/W7-2023, 193–200. https://doi.org/10.5194/isprs-archives-xlviii-4-w7-2023-193-2023

Su, X., Shen, Y., Zhou, W., Liu, Y., Cheng, H., & Liu, G. (2022). Mapping forest disturbance and recovery for ecological security improvement on the Qinghai-Tibet Plateau: A case study from Three Parallel Rivers Region. Global Ecology and Conservation, 33, e01983. https://doi.org/10.1016/j.gecco.2021.e01983

Tamiminia, H., Salehi, B., Mahdianpari, M., Quackenbush, L. J., Adeli, S., & Brisco, B. (2020). Google Earth Engine for geo-big data applications: A meta-analysis and systematic review. ISPRS Journal of Photogrammetry and Remote Sensing, 164, 152–170. https://doi.org/10.1016/j.isprsjprs.2020.04.001

Wang, Y., Guo, E., Kang, Y., & Ma, H. (2022). Assessment of land desertification and its drivers on the Mongolian plateau using intensity analysis and the geographical detector technique. Remote Sensing, 14(24), 6365. https://doi.org/10.3390/rs14246365

Downloads

Published

2026-09-01

How to Cite

Yasien, H. M., Sucitra, I. G. N. B., & Djatmiko, M. (2026). Remote Sensing Big Data Analysis to Enhance Strategic Intelligence Capability in National Security. Security Intelligence Terrorism Journal (SITJ), 3(3), 238–243. https://doi.org/10.70710/sitj.v3i3.110

Issue

Section

Articles

Similar Articles

<< < 1 2 3 4 5 6 7 8 9 > >> 

You may also start an advanced similarity search for this article.