Geospatial intelligent analysis to support Indonesian airspace defense
Keywords:
airspace defense, geospatial intelligence, remote sensing, satellite imagery, unmanned aerial vehiclesAbstract
Background: In the modern era, air defense plays a critical role in national security strategies, especially for Indonesia, the world's largest archipelagic nation, which faces complex challenges in safeguarding its vast airspace. Geospatial Intelligence (GEOINT) offers advanced capabilities for enhancing detection, mapping, and planning in air defense operations. By utilizing data from satellites, UAVs, and sensor technologies, GEOINT has the potential to significantly improve air defense through real-time data analysis and situational modeling. Methods: This study employed a literature review method to examine the role of GEOINT in Indonesia's air defense. Literature from academic sources and official reports was analyzed, focusing on peer-reviewed studies that discuss GEOINT technologies such as UAVs, radar systems, and topographic mapping. The review covered the application of these technologies in threat detection, mission planning, and the surveillance of adversarial activities. Findings: The analysis revealed that GEOINT is vital in supporting Indonesia's air defense operations. It enables early threat detection, efficient mission planning, and constant monitoring of adversarial movements using data from satellites and UAVs. Additionally, GEOINT facilitates accurate terrain mapping, optimal flight route planning, and the surveillance of strategic areas, enhancing defense strategies and enabling timely preventive actions against threats. Conclusion: GEOINT is an essential strategic tool for enhancing Indonesia’s air defense capabilities. To maximize its potential, investments in advanced technologies, geospatial data infrastructure, and personnel training are crucial. Furthermore, international collaboration and the development of monitoring satellites will be pivotal for the future success of Indonesia’s air defense strategy. Novelty/Originality of this article: This study provides a focused exploration of the integration of GEOINT in Indonesia's air defense strategy, highlighting its transformative impact on threat detection and defense planning. The study underscores the need for technological advancement and international partnerships in strengthening national air defense capabilities.
References
Adade, R., Aibinu, A. M., Ekumah, B., & Asaana, J. (2021). Unmanned Aerial Vehicle (UAV) applications in coastal zone management—a review. In Environmental Monitoring and Assessment (Vol. 193, Issue 3). https://doi.org/10.1007/s10661-021-08949-8
Amrutha, C. V., Jyotsna, C., & Amudha, J. (2020). Deep Learning Approach for Suspicious Activity Detection from Surveillance Video. 2nd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2020 - Conference Proceedings. https://doi.org/10.1109/ICIMIA48430.2020.9074920
Babel, L. (2023). Coordinated flight path planning for a fleet of missiles in high-risk areas. Robotica, 41(5). https://doi.org/10.1017/S0263574722001886
Barik, P. K., Shah, S., Shah, K., Modi, A., & Devisha, H. (2022). UAV-Assisted Surveillance Using Machine Learning. PDGC 2022 - 2022 7th International Conference on Parallel, Distributed and Grid Computing. https://doi.org/10.1109/PDGC56933.2022.10053282
Bell, J. E., Griffis, S. E., Cunningham, W. A., & Eberlan, J. A. (2011). Location optimization of strategic alert sites for homeland defense. Omega, 39(2). https://doi.org/10.1016/j.omega.2010.05.004
Bhattacharya, S., Czejdo, B., & Malhotra, R. (2013). Geospatial intelligence as a context for computing education (abstract only). https://doi.org/10.1145/2445196.2445450
Boccardo, P., & Gentili, G. (2012). HIGH RESOLUTION DSM AND CLASSIFIED VOLUMETRIC GENERATION: AN OPERATIONAL APPROACH TO THE IMPROVEMENT OF GEOSPATIAL INTELLIGENCE. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVIII-4/W19. https://doi.org/10.5194/isprsarchives-xxxviii-4-w19-45-2011
Coorey, R. S. (2018). The evolution of geospatial intelligence. In Advances in Military Geosciences. https://doi.org/10.1007/978-3-319-73408-8_10
Dangermond, J., & Goodchild, M. F. (2020). Building geospatial infrastructure. Geo-Spatial Information Science, 23(1). https://doi.org/10.1080/10095020.2019.1698274
Dogru, S., & Marques, L. (2020). Pursuing Drones with Drones Using Millimeter Wave Radar. IEEE Robotics and Automation Letters, 5(3). https://doi.org/10.1109/LRA.2020.2990605
Einaudi, F., Uccellini, L., Purdom, J., Rogers, D., Gelaro, R., Dodge, J., Atlas, R., & Lord, S. (2001). Weather prediction improvement using advanced satellite technology. International Geoscience and Remote Sensing Symposium (IGARSS), 1. https://doi.org/10.1109/igarss.2001.976086
Elgamoudi, A., Benzerrouk, H., Elango, G. A., & Landry, R. (2021). A survey for recent techniques and algorithms of geolocation and target tracking in wireless and satellite systems. Applied Sciences (Switzerland), 11(13). https://doi.org/10.3390/app11136079
Evans, C. V. (2020). Future warfare: Weaponizing critical infrastructure. Parameters, 50(2). https://doi.org/10.55540/0031-1723.1017
Eyre, J. R., Bell, W., Cotton, J., English, S. J., Forsythe, M., Healy, S. B., & Pavelin, E. G. (2022). Assimilation of satellite data in numerical weather prediction. Part II: Recent years. In Quarterly Journal of the Royal Meteorological Society (Vol. 148, Issue 743). https://doi.org/10.1002/qj.4228
Gao, K., Xiao, H., Qu, L., & Wang, S. (2022). Optimal interception strategy of air defence missile system considering multiple targets and phases. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 236(1). https://doi.org/10.1177/1748006X211022111
Guanglei, M., Runnan, Z., Biao, W., Mingzhe, Z., Yu, W., & Xiao, L. (2021). Target Tactical Intention Recognition in Multiaircraft Cooperative Air Combat. International Journal of Aerospace Engineering, 2021. https://doi.org/10.1155/2021/9558838
Khalaf-Allah, M. (2021). Emitter location with azimuth and elevation measurements using a single aerial platform for electronic support missions. Sensors, 21(12). https://doi.org/10.3390/s21123946
Laouira, M. L., Abdelli, A., Othman, J. Ben, & Kim, H. (2021). An Efficient WSN Based Solution for Border Surveillance. IEEE Transactions on Sustainable Computing, 6(1). https://doi.org/10.1109/TSUSC.2019.2904855
Li, W. (2022). Big Data Precision Marketing Approach under IoT Cloud Platform Information Mining. Computational Intelligence and Neuroscience, 2022. https://doi.org/10.1155/2022/4828108
Liu, M., Li, B., Chen, Y., Yang, Z., Zhao, N., Liu, P., & Gong, F. (2022). Location Parameter Estimation of Moving Aerial Target in Space-Air-Ground-Integrated Networks-Based IoV. IEEE Internet of Things Journal, 9(8). https://doi.org/10.1109/JIOT.2021.3071927
Liu, M., Liu, C., Li, M., Chen, Y., Zheng, S., & Zhao, N. (2022). Intelligent passive detection of aerial target in space-air-ground integrated networks. China Communications, 19(1). https://doi.org/10.23919/JCC.2022.01.005
Mazzoleni, M., Paron, P., Reali, A., Juizo, D., Manane, J., & Brandimarte, L. (2020). Testing UAV-derived topography for hydraulic modelling in a tropical environment. Natural Hazards, 103(1). https://doi.org/10.1007/s11069-020-03963-4
McEneaney, W. M., Fitzpatrick, B. G., & Lauko, I. G. (2004). Stochastic game approach to air operations. IEEE Transactions on Aerospace and Electronic Systems, 40(4). https://doi.org/10.1109/TAES.2004.1386874
Miccinesi, L., Bigazzi, L., Consumi, T., Pieraccini, M., Beni, A., Boni, E., & Basso, M. (2022). Geo-Referenced Mapping through an Anti-Collision Radar Aboard an Unmanned Aerial System. In Drones (Vol. 6, Issue 3). https://doi.org/10.3390/drones6030072
Mobley, F. S., Wall, A. T., & Campbell, S. C. (2021). Translating jet noise measurements to near-field level maps with nearest neighbor bilinear smoothing interpolation. The Journal of the Acoustical Society of America, 150(2). https://doi.org/10.1121/10.0005737
Moldt, V. A. (2003). Remote sensing for homeland defense/emergency response. Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Defense and Law Enforcement II, 5071. https://doi.org/10.1117/12.500235
Mugavero, R., Benolli, F., & Sabato, V. (2015). Geospatial Intelligence, Technological Development, and Human Interaction. Journal of Information Privacy and Security, 11(4). https://doi.org/10.1080/15536548.2015.1105652
Naseem, A., & Ahmad, Y. (2020). Critical Success Factors for Neutralization of Airborne Threats. SAGE Open, 10(3). https://doi.org/10.1177/2158244020963066
Nijim, M. (2016). Multitasking intelligent surveillance and first response system. 2016 IEEE Symposium on Technologies for Homeland Security, HST 2016. https://doi.org/10.1109/THS.2016.7568935
Panagiotou, E., Chochlakis, G., Grammatikopoulos, L., & Charou, E. (2020). Generating elevation surface from a single RGB remotely sensed image using deep learning. Remote Sensing, 12(12). https://doi.org/10.3390/rs12122002
Park, J. K., Jung, K. Y., & Heo, J. H. (2020). Statistical analysis for usability evaluation of unmanned aerial vehicle in geomatics. Sensors and Materials, 32(12). https://doi.org/10.18494/SAM.2020.2912
Partsinevelos, P., & Su, H. (2022). Special Section Guest Editorial: Unmanned Systems and Satellites: a Synergy for Added-value Possibilities. Journal of Applied Remote Sensing, 16(02). https://doi.org/10.1117/1.jrs.16.022201
Pierdicca, R., & Paolanti, M. (2022). GeoAI: a review of artificial intelligence approaches for the interpretation of complex geomatics data. In Geoscientific Instrumentation, Methods and Data Systems (Vol. 11, Issue 1). https://doi.org/10.5194/gi-11-195-2022
Praveen, P., Jayanth Babu, C. H., & Rama, B. (2016). Big data environment for geospatial data analysis. Proceedings of the International Conference on Communication and Electronics Systems, ICCES 2016. https://doi.org/10.1109/CESYS.2016.7889816
Pundir, S. K., & Garg, R. D. (2020). Development of mapping techniques for off road trafficability to support military operation. Spatial Information Research, 28(4). https://doi.org/10.1007/s41324-019-00310-z
Radočaj, D., Obhođaš, J., Jurišić, M., & Gašparović, M. (2020). Global open data remote sensing satellite missions for land monitoring and conservation: A review. In Land (Vol. 9, Issue 11). https://doi.org/10.3390/land9110402
Riabukha, V. P. (2020). Radar Surveillance of Unmanned Aerial Vehicles (Review). Radioelectronics and Communications Systems, 63(11). https://doi.org/10.3103/S0735272720110011
Rocca, F., Li, D., Tebaldini, S., Liao, M., Zhang, L., Lombardini, F., Balz, T., Haala, N., Ding, X., & Hanssen, R. (2021). Three‐ and four‐dimensional topographic measurement and validation. Remote Sensing, 13(15). https://doi.org/10.3390/rs13152861
Santi, F., Blasone, G. P., Pastina, D., Colone, F., & Lombardo, P. (2021). Parasitic surveillance potentialities based on a GEO-SAR illuminator. Remote Sensing, 13(23). https://doi.org/10.3390/rs13234817
Shan, X. G., & Zhuang, J. (2020). A game-theoretic approach to modeling attacks and defenses of smart grids at three levels. Reliability Engineering and System Safety, 195. https://doi.org/10.1016/j.ress.2019.106683
Shukla, A., & Jain, K. (2020). Automatic extraction of urban land information from unmanned aerial vehicle (UAV) data. Earth Science Informatics, 13(4). https://doi.org/10.1007/s12145-020-00498-x
Solla, M., Casqueiro, C., & del Cuvillo, I. (2020). Approach to generate 3D-printed terrain models using free software and open data sources: Application to military planning. Computer Applications in Engineering Education, 28(3). https://doi.org/10.1002/cae.22211
Stecz, W., & Gromada, K. (2020). UAV mission planning with SAR application. Sensors (Switzerland), 20(4). https://doi.org/10.3390/s20041080
Summers, D. S., Robbins, M. J., & Lunday, B. J. (2020). An approximate dynamic programming approach for comparing firing policies in a networked air defense environment. Computers and Operations Research, 117. https://doi.org/10.1016/j.cor.2020.104890
Taghavi, E., Song, D., Tharmarasa, R., Kirubarajan, T., McDonald, M., Balaji, B., & Brown, D. (2020). Geo-registration and Geo-location Using Two Airborne Video Sensors. IEEE Transactions on Aerospace and Electronic Systems, 56(4). https://doi.org/10.1109/TAES.2020.2995439
Touzopoulos, P., & Zikidis, K. C. (2021). Physical Optics Radar Cross Section predictions for an anti-ship cruise missile. Journal of Defense Modeling and Simulation. https://doi.org/10.1177/15485129211033039
Tung, F., Zelek, J. S., & Clausi, D. A. (2011). Goal-based trajectory analysis for unusual behaviour detection in intelligent surveillance. Image and Vision Computing, 29(4). https://doi.org/10.1016/j.imavis.2010.11.003
Utomo, A. M., Wijayanto, G. N., Yusfan, M. A., Wardani, P., Poniman, A., Supriyadi, A. A., Gultom, R. A. G., Martha, S., Purwantoro, S. A., & Arief, S. (2021). Geospatial Intelligence Analysis to Support National Defense Interests. 2021 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2021. https://doi.org/10.1109/ICACSIS53237.2021.9631348
Woo, J. W., Choi, Y. S., An, J. Y., & Kim, C. J. (2022). An Approach to Air-to-Surface Mission Planner on 3D Environments for an Unmanned Combat Aerial Vehicle. Drones, 6(1). https://doi.org/10.3390/drones6010020
Xu, Q., Ge, J., Yang, T., & Sun, X. (2020). A trajectory design method for coupling aircraft radar cross-section characteristics. Aerospace Science and Technology, 98. https://doi.org/10.1016/j.ast.2019.105653
Downloads
Published
Issue
Section
Citation Check
License
Copyright (c) 2024 Remote Sensing Technology in Defense and Environment
This work is licensed under a Creative Commons Attribution 4.0 International License.
