Boreas Navigator

The problem

Arctic vessel route planning is a manual, fragmented process. Ice analysts derive ice-edge, ice-type, and concentration estimates from satellite imagery and operational ice products, close-range hazard detection comes from separate onboard sensors, and the slow assimilation of ice movement remains the operational bottleneck. The result is a high-consequence routing decision made from disconnected, deterministic inputs.

Our approach

• •Unified pipeline – Boreas Navigator collapses ice retrieval, forecasting, and route planning into a single decision aid that a navigation team interacts with like a GPS.
• •Multi-modal retrieval – Calibrated deep learning across SAR, passive microwave, and optical imagery, with a physics-aware model prior.
• •Calibration as a first-class objective – The model is trained to be well-calibrated, not just accurate, so its confidence can be trusted.
• •Uncertainty propagated end-to-end – Conformal prediction sets feed a risk-bounded route planner, so navigation decisions account for what the model does and does not know.

What makes it different

Most sea-ice systems present a single deterministic estimate, a line on a chart. Boreas Navigator instead delivers probabilistic ice fields with visible, well-calibrated confidence, and carries that uncertainty all the way through to the route recommendation. That trust-first posture is the right one for high-consequence polar navigation.

Status

Boreas Navigator is an active research and development effort. It builds directly on our published work in calibrated multi-modal forecasting and uncertainty quantification, and is being developed in collaboration with academic partners in Arctic system modeling.