GIS-Integrated Convolutional Neural Network Framework for Real-Time Border Threat Detection

The rapid proliferation of inexpensive unmanned aerial vehicles, cross-border smuggling routes, and illicit incursions demands intelligent surveillance that fuses spatial context with automated visual analytics. We present a GIS-integrated Convolutional Neural Network (CNN) framework that ingests live video streams from fixed or mobile cameras, classifies each frame for “threat” versus “no-threat,” and geo-locates positive detections on an interactive map to support command-and-control decisions. Implemented and evaluated entirely in Google Colab, the prototype achieves 93.8 % accuracy, 0.92 precision, and 0.91 recall on a 4,487-frame benchmark compiled from open-source border videos. The system overlays detections on ESRI- compliant shape-files in real time, demonstrating sub-250 ms end-to-end latency per frame. Extensive experiments confirm that spatial filtering (masking detections outside polygonal border zones) reduces false alarms by 27 % without harming recall. The proposed architecture is low- cost, cloud-agnostic, and readily extensible to additional object classes or sensor modalities.