Geospatial Smart Technologies in Military Strategy: Lessons from the Iran–Israel–US Conflict

Geospatial smart technologies have emerged as decisive instruments in the transformation of contemporary military strategy, reshaping how states compress decision cycles, enhance situational awareness, and integrate precision targeting into operational planning. Against this backdrop, the study sets out to interrogate the strategic role of geospatial intelligence—satellite-based remote sensing, AI-driven targeting systems, and drone swarm navigation—within the triangular confrontation involving Iran, Israel, and the United States. Methodologically, the research adopts a comparative case study approach, drawing on open-source intelligence, defense analyses, and scholarly literature to evaluate how these actors deploy geospatial technologies as both tactical enablers and strategic determinants of deterrence and escalation management. The findings reveal distinct patterns: the United States sustains global surveillance dominance through space-based remote sensing and algorithmic kill-chain compression; Israel integrates AI-enabled targeting systems such as Lavender and The Gospel with missile defense architectures like Iron Dome; while Iran pursues asymmetric strategies including GPS spoofing and drone swarms to disrupt adversary geospatial superiority. Three critical dynamics emerge from this comparison: the acceleration of military kill chains through AI–geospatial fusion, the destabilizing potential of asymmetric geospatial tactics, and the transparency effects of commercial satellite imagery on escalation control. The study concludes that geospatial technologies simultaneously enhance operational efficiency and introduce novel vulnerabilities. This duality underscores the urgent need for international governance frameworks that balance technological innovation with humanitarian protection, accountability, and stability in future conflicts.

Geospatial Intelligence, AI-Enabled Targeting Systems, Drone Swarm Navigation, Deterrence and Escalation Management

1. Alberts, D. S., Garstka, J. J., & Stein, F. P. (1999). Network centric warfare: Developing and leveraging information superiority. CCRP Publication Series. [Google Scholar] [Crossref]

2. Clausewitz, C. von. (1976). On war (M. Howard & P. Paret, Eds. & Trans.). Princeton University Press. (Original work published 1832) [Google Scholar] [Crossref]

3. Coco, A. (2025). Accountability in autonomous targeting: Legal responsibility in algorithmic warfare. Journal of International Humanitarian Law, 27(2), 145–167. https://doi.org/10.1017/jihl.2025.12 [Google Scholar] [Crossref]

4. Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE Publications. [Google Scholar] [Crossref]

5. Dorsey, J. L. (2026). The erosion of human(e) judgement in targeting? Quantification logics, AI-enabled decision support systems and proportionality assessments in IHL. International Review of the Red Cross, 107(930), 1041–1071. https://doi.org/10.1017/S1816383125100969 [Google Scholar] [Crossref]

6. Gregory, D. (2019). Geopolitics and military geospatial intelligence. Routledge. [Google Scholar] [Crossref]

7. International Committee of the Red Cross. (2024). ICRC policy on artificial intelligence and machine learning in armed conflict. Geneva: ICRC. https://www.icrc.org [Google Scholar] [Crossref]

8. International Committee of the Red Cross. (2026). Position paper on autonomous weapon systems and humanitarian law. Geneva: ICRC. https://www.icrc.org [Google Scholar] [Crossref]

9. Johnson, R. (2024). Geospatial intelligence and modern warfare: Strategic implications for global security. Cambridge University Press. [Google Scholar] [Crossref]

10. Katz, M. (2025). Missile defense and geospatial technologies in the Middle East. Journal of Strategic Studies, 48(2), 215–232. https://doi.org/10.1080/01402390.2025.1234567 [Google Scholar] [Crossref]

11. Khan, R. (2025). Technology neutrality and the Geneva Conventions: Challenges of AI-enabled targeting. Military Law Review, 233(3), 201–223. https://doi.org/10.1093/mlr/233.3.201 [Google Scholar] [Crossref]

12. Krepinevich, A. F. (1994). Cavalry to computer: The pattern of military revolutions. The National Interest, 37, 30–46. [Google Scholar] [Crossref]

13. Patton, M. Q. (2015). Qualitative research & evaluation methods (4th ed.). SAGE Publications. [Google Scholar] [Crossref]

14. Shurkin, M. (2025). Artificial intelligence and targeting in modern conflict. International Security, 50(1), 87–112. https://doi.org/10.1162/isec_a_00456 [Google Scholar] [Crossref]

15. Simmons, A. (2026). The algorithmic battlespace: AI and geospatial fusion in military strategy. Project Geospatial. [Google Scholar] [Crossref]

16. United Nations General Assembly. (2024). Resolution on lethal autonomous weapons systems and international humanitarian law (A/RES/78/221). New York: United Nations. https://www.un.org [Google Scholar] [Crossref]

17. United Nations General Assembly. (2025). Resolution on governance of emerging military technologies (A/RES/79/114). New York: United Nations. https://www.un.org [Google Scholar] [Crossref]

18. Wiley. (2026). Modern approaches in military geoscience: Leveraging advanced geospatial technologies for strategic advantage. Wiley Online Library. [Google Scholar] [Crossref]

19. Yin, R. K. (2018). Case study research and applications: Design and methods (6th ed.). SAGE Publications. [Google Scholar] [Crossref]

• LeafQuest: A Mobile-Based Augmented Reality for Plant Placement, Discovery, and Growth
• Participatory Ergonomic Intervention Approach on Musculoskeletal Disorder (MSD) in Construction Sectors: A Systematic Review
• Integrating GIS into Traffic Incident Management: A Web-Based System
• RideSmart: A Personalized Motorcycle Product Recommendation System Using TF-IDF and Descriptive Analytics for Javidson Motorshop
• Educational Technology Course Design in Pre-Service Teachers Education: A Bibliometric Review of the Research Landscape