Detecting Systematic Infrastructure Attacks via Geospatial Intelligence

Abstract

Modern warfare increasingly targets infrastructure indispensable to civilian survival, particularly water and food systems, as a tactical instrument of coercion. Despite extensive reporting, a capability gap persists in scalable tools for real-time damage detection and humanitarian impact prediction. This paper introduces an integrated geospatial artificial intelligence (GeoAI) framework leveraging multi-modal satellite imagery, computer vision, and data fusion to map vulnerabilities in infrastructure and supply chains. By fusing pixel-level change detection with ACLED conflict data and WFP logistics telemetry, we generate spatially explicit predictive risk indicators. We evaluate this toolkit through case studies on the destruction of the water-energy nexus in Ukraine and the food system blockades in Yemen. Contributions include a scalable OSINT toolkit, a multi-tiered early-warning typology, and a prototype dashboard architecture for crisis management. Finally, we address ethical considerations regarding demographically identifiable information (DII), demonstrating how bridging GeoAI with humanitarian analysis advances early-warning capabilities for national security and global response.