Embed the world: Multimodal AI for searchable aerial imagery at scale | Amazon Web Services
Turning a library of aerial imagery into a natural-language-searchable knowledge base is a problem that touches every industry that relies on geospatial data — insurance, real estate, government, infrastructure, and agriculture. The traditional path requires either manual tile-by-tile inspection or training a bespoke computer vision model for each new question. Multimodal embeddings, large language model (LLM) captioning, and vector search on AWS offer a faster alternative: index once, then query using natural language.
We worked with Vexcel, an aerial imagery and geospatial data provider that operates one of the largest aerial imagery programs in the world, to evaluate embedding models, fusion strategies, caption integration, and search methods over multi-view aerial imagery. Using its own sensors and a dedicated fleet of aircraft, Vexcel collects high-resolution data across 45+ countries and territories, delivering orthomosaic imagery, oblique imagery from multiple angles, and elevation models. The data exists, and the use cases are...
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