Integrating Earth observation IMINT with OSINT data to create added-value multisource intelligence information: A case study of the Ukraine–Russia war

• Autorzy: Kotaridis Ioannis , Benekos Georgios

Identyfikatory

DOI

10.35467/sdq/170901

• Języki publikacji: EN

Abstrakty

EN

The Russian invasion of Ukraine on 24 February 2022 heralded a new “social media war” era. This “hybrid warfare” extends beyond the military landscape and includes attacks in cyberspace and fake news with the aim of destabilising governments. The goal of this paper is to present a high-level of architecture based on imagery intelligence (IMINT) and geospatial intelligence (GEOINT) using geographic datasets and state-of-the-art methods. Integration with intelligence information (like Open-Source Intelligence [OSINT]) produces multiintelligent knowledge for security and defence decision-making end users. The results depict a harmonious and creative collaboration between IMINT, OSINT, and GEOINT. OSINT data helps to identify and describe the meteorological conditions that are present, contributing to the procedure’s responsiveness. Weather and dense cloud cover above Ukraine poses a challenge for optical imaging satellites, but synthetic aperture radar (SAR) sensor satellites can operate at night and overcome the problem. We carried out OSINT and IMINT analysis, monitoring the situation shortly after the invasion. OSINT data helped in the choice of an appropriate area of interest. Using the right Earth observation satellite system and artificial intelligence/machine learning algorithms is the best way to keep an eye on many different sites over long periods, send out alerts about unusual activity, and find new places where incoherent changes might be happening.

Słowa kluczowe

EN

artificial intelligence; Earth observation; IMINT; OSINT; Russia–Ukraine hybrid war

• Wydawca: Akademia Sztuki Wojennej
• Czasopismo: Security and Defence Quarterly
• Rocznik: 2023
• Tom: Vol. 43, No. 3
• Strony: 1--21
• Opis fizyczny: Bibliogr. 25 poz., rys.

Twórcy

autor

Kotaridis Ioannis

• Geospatial Function, Planetek Hellas, Leoforos Kifisias 44, 15125, Athens, Greece
• Aristotle University of Thessaloniki, Faculty of Engineering, School of Civil Engineering, Lab. of Photogrammetry – Remote Sensing, 54124 Thessaloniki, Greece

• https://orcid.org/0000-0003-0876-2627

autor

Benekos Georgios

• Geospatial Function, Planetek Hellas, Leoforos Kifisias 44, 15125, Athens, Greece

• https://orcid.org/0009-0005-9676-5124

Bibliografia

• 1. Abramowicz V. (2022) Military operations in a more transparent world. Available at: https://.lowyinstitute.org/the-interpreter/military-operations-more-transparent-world (Accessed: 24 August 2023).
• 2. AIRBUS (2023) Multi-INT Exploitation - Delivering the complete picture for informed decision-making. Available at: https://.intelligence-airbusds.com/markets/defence/joint-isr/multi-int-exploitation (Accessed: 24 August 2023).
• 3. Alzubaidi L., Zhang J., Humaidi A.J., Al-Dujaili A., Duan Y., Al-Shamma O., Santamaría J., Fadhel M.A., Al-Amidie M. and Farhan L. (2021) ‘Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions’, Journal of Big Data, 8(1), p. 53. doi 10.1186/s40537-021-00444-8.
• 4. Bimfort M. T. (1994) ‘Intelligence as a Science’, Studies in Intelligence, 2(2), p. 76.
• 5.Copernicus Sentinel data (2022), processed by European Space Agency. Available at: https://search.asf.alaska.edu/ (Accessed: 24 August 2023).
• 6. Du B., Ru L., Wu C. and Zhang L. (2019) ‘Unsupervised deep slow feature analysis for change detection in multi-temporal remote sensing images’, IEEE Transactions on Geoscience and Remote Sensing, 57(12), pp. 9976–9992. doi: 10.1109/TGRS.2019.2930682.
• 7. Federation of American Scientists (2023) Intelligence collection activities and disciplines. Available at: https://irp.fas.org/nsa/ioss/threat96/part02.htm#:~:text=Several%20intelligence%20disciplines%20are%20used,open%20source%20intelligence%20(OSINT) (Accessed: 24 August 2023).
• 8. Fang S., Li K., Shao J. and Li Z. (2022) ‘SNUNet-CD: A densely connected siamese network for change detection of VHR images’, IEEE Geoscience and Remote Sensing Letters, 19, pp. 1–5. doi: 10.1109/LGRS.2021.3056416.
• 9. Gao F., Dong J., Li B. and Xu Q. (2016) ‘Automatic change detection in synthetic aperture radar images based on PCANet’, IEEE Geoscience and Remote Sensing Letters, 13(12), pp. 1792–1796. doi: 10.1109/LGRS.2016.2611001.
• 10. Groce A. (2022) LibGuides: Intelligence studies: types of intelligence collection. Available at: https://usnwc.libguides.com/c.php?g=494120&p=3381426 (Accessed: 11 June 2022).
• 11. Karamvasis K., Bliziotis D., Backfried G., Thomas-Aniola D. and Pfeiffer M. (2021) ‘Exploring links between EO Satellites, social media and crowdsourcing information against terrorism and organized crime’, European Commission, Joint Research Centre, Albani, S., Loekken, S., Soille, P. Proceedings of the 2021 conference on Big Data from Space : 18-20 May 2021. Publications Office. pp. 41-44. https://data.europa.eu/doi/10.2760/125905.
• 12. Khan M.M., Ghani I., Jeong S.R., Ibrahim R., and Rehman H. (2016) Social media usage in academic research. Journal of Theoretical and Applied Information Technology, 87 (2), 191–197.
• 13. Li Z., Wang Y., Zhang N., Zhang Y., Zhao Z., Xu D., Ben G. and Gao Y. (2022) ‘Deep learning-based object detection techniques for remote sensing images: A survey’, Remote Sensing, 14(10), p. 2385. doi: 10.3390/rs14102385.
• 14. Liu J., Gong M., Qin K. and Zhang P. (2018) ‘A deep convolutional coupling network for change detection based on heterogeneous optical and radar images’, IEEE Transactions on Neural Networks and Learning Systems, 29(3), pp. 545–559. doi: 10.1109/TNNLS.2016.2636227.
• 15. Luppino L.T., Kampffmeyer M., Bianchi F.M., Moser G., Serpico S.B., Jenssen R. and Anfinsen S.N. (2022) ‘Deep image translation with an affinity-based change prior for unsupervised multimodal change detection’, IEEE Transactions on Geoscience and Remote Sensing, 60, pp. 1–22. doi: 10.1109/TGRS.2021.3056196.
• 16. Martin (1994) Civilian Intelligence Personnel Management System (CIPMS), GS-132 Intelligence Specialist Series - Intelligence Operations (Appendix D Part II). Available at: https://.dami.army.pentagon.mil/offices/dami-cp/guidance/aogs/132_st/part_II.asp (Accessed: 24 August 2023).
• 17. Munir A., Aved A. and Blasch E. (2022) ‘Situational awareness: Techniques, challenges, and prospects’, AI, 3(1), pp. 55–77. doi: 10.3390/ai3010005.
• 18. Qu X., Gao F., Dong J., Du Q. and Li H.-C. (2022) ‘Change detection in synthetic aperture radar images using a dual-domain network’, IEEE Geoscience and Remote Sensing Letters, 19, pp. 1–5. doi: 10.1109/LGRS.2021.3073900.
• 19. Strick (2023) Eyes on Russia: Documenting Russia’s war on Ukraine. Available at: https://.info-res.org/post/eyes-on-russia-documenting-conflict-and-disinformation-in-the-kremlin-s-war-on-ukraine (Accessed: 24 August 2023).
• 20. Tapete D. and Cigna F. (2019) ‘Detection of archaeological looting from space: Methods, achievements and challenges’, Remote Sensing, 11(20), p. 2389. doi: 10.3390/rs11202389.
• 21. Toivonen T., Heikinheimo V., Fink C., Hausmann A., Hiippala T., Järv O., Tenkanen H. and Di Minin E. (2019) ‘Social media data for conservation science: A methodological overview’, Biological Conservation, 233, pp. 298–315. doi: 10.1016/j.biocon.2019.01.023.
• 22. Tropin Z. (2021) ‘Lawfare as part of hybrid wars: The experience of Ukraine in conflict with Russian Federation’, Security and Defence Quarterly, 33(1), pp. 15–29. doi: 10.35467/sdq/132025.
• 23. Wu C., Chen H., Du B. and Zhang L. (2021) ‘Unsupervised change detection in multitemporal VHR images based on deep kernel PCA convolutional mapping network’, IEEE Transactions on Cybernetics, 52(11), pp. 1–15. doi: 10.1109/TCYB.2021.3086884.
• 24. Zhang X., Su H., Zhang C., Gu X., Tan X. and Atkinson P.M. (2021) ‘Robust unsupervised small area change detection from SAR imagery using deep learning’, ISPRS Journal of Photogrammetry and Remote Sensing, 173, pp. 79–94. doi: 10.1016/j.isprsjprs.2021.01.004.
• 25. Ziółkowska A. (2018) ‘Open source intelligence (OSINT) as an element of military recon’, Security and Defence Quarterly, 19(2), pp. 65–77. doi: 10.5604/01.3001.0012.1474.