Imagine Google Earth that not only shows you pictures of Earth but also automatically tells you what changed, where ships and planes moved, where forests were cut, or where construction started—without humans scanning millions of images. That’s what AI on satellite imagery does: it turns raw pictures from space into searchable, real-time alerts and maps.
Traditional satellite imagery requires large analyst teams to manually inspect images, making it slow, expensive, and easy to miss important changes. AI automates detection, classification, and monitoring of objects and patterns (troop movements, ships, infrastructure, deforestation, disaster impacts), dramatically speeding up decision-making for defense, intelligence, and commercial users.
Access to a proprietary, high-cadence global imagery archive combined with AI models tuned on that data; tight integration into defense and intelligence workflows; long-standing relationships with government and commercial GEOINT buyers.
Hybrid
Vector Search
High (Custom Models/Infra)
Compute and storage costs for processing, storing, and querying global, high-cadence imagery at petabyte scale; plus latency/throughput for running vision + LLM pipelines for many concurrent intelligence queries.
Early Majority
This use case emphasizes end-to-end AI pipelines that convert continuous satellite imagery streams into structured, queryable intelligence products (alerts, analytics layers, and natural-language answers), rather than just selling imagery; integration of LLM-style interfaces on top of computer-vision-derived features is a newer differentiator versus traditional imagery providers.