How the Coordinate Embedding Framework, Spatial Neural Network, and Multi-Agent Collaboration Protocol fit together — from raw geographic inputs to theorem-backed risk classifications on 546,000+ California addresses.
A GeoAI architecture that processes coordinates as semantic entities, not static geographic data — enabling real-time reasoning at address scale.
Raw lat/lng maps into a 512-dimensional semantic space with a proven bi-Lipschitz guarantee — spatial distances survive embedding within bounded distortion.
Four specialized agent pools (fire, flood, seismic, analytics) reach weighted consensus with provable convergence and Byzantine fault tolerance up to 10 nodes.
How coordinate embedding, spatial neural networks, and a 128-agent protocol compose end-to-end: from raw lat/lng to validated risk assessments in under 200 ms.
Evaluated end-to-end against California fire-hazard data with published methodology and theorem-backed bounds.
The architecture above is backed by three peer-style publications with proofs, benchmarks, and reproducible evaluation. Download the PDFs or read the web previews on the publications page.