This is like putting a small, smart “brain” directly on a satellite so it can look at disaster areas (floods, fires, storms), understand what’s happening in real time, and send only the most important information down to responders instead of dumping all the raw images.
Traditional satellites send huge volumes of raw imagery to the ground, which is slow, bandwidth‑intensive, and too late for fast‑moving disasters. Onboard AI and edge computing let satellites analyze imagery in space and push timely, actionable alerts to emergency managers with far less data transmission.
Tight integration of AI models with specific satellite hardware, orbital data, and disaster-management workflows; potential proprietary training data from historical disaster imagery and partnerships with space agencies and civil protection authorities.
Hybrid
Vector Search
High (Custom Models/Infra)
Onboard compute and power limits on satellites, plus constraints on updating models once deployed in orbit.
Early Adopters
Focus on running AI inference directly on satellites (true edge computing in orbit) for disaster intelligence, rather than relying purely on ground-based processing of satellite data. This reduces latency and bandwidth needs and is tailored for emergency and disaster-management use cases.