Multiverse Computing partnered with a transnational defense and security organization to deliver a sovereign, edge-deployable AI capability for border surveillance over massive geographies. The project demonstrates how heavy optimization of state-of-the-art vision models turns wide-area monitoring into a routine, scalable operation, even on constrained hardware, and gives the client full control over the data, models and inference behind a mission-critical capability.
The Challenge
The client needed to perform real-time object detection over more than 670,000 km² using high-resolution, multi-spectral satellite imagery, including RGB, infrared and synthetic aperture radar. The data volume, the image resolution (30 cm per pixel) and the refresh requirement (multiple inferences per hour per location) placed significant pressure on infrastructure and energy budgets, while the mission context required no compromise on accuracy. Standard object detection models were too heavy and too slow to be viable within the deployment footprint required.