GNSS is a critical part of modern navigation, but signal loss, degradation, and positioning uncertainty still occur. As someone working in airborne LiDAR and currently studying transportation route geometry, I started wondering whether roadway geometry itself could help maintain location awareness when GNSS becomes unreliable.

This idea became what I call Spatial Memory Systems. The concept is simple: vehicles move through transportation corridors that already contain known geometric relationships such as alignments, stationing, tangents, curves, and roadway constraints. Instead of relying entirely on live GNSS observations, could those geometric relationships help reduce uncertainty when positioning data is lost?

Using open geospatial data and open-source tools, this project explores whether roadway geometry can act as an additional source of navigational context during simulated GNSS outages. The initial proof of concept will use publicly available roadway data and a simulated vehicle moving along a route. GNSS observations will be intentionally removed and compared against position estimates constrained by route geometry.

This is an early-stage concept rather than a finished solution. The goal of this lightning talk is to introduce the idea, show how route geometry could potentially support navigation during GNSS outages, and gather feedback from the FOSS4G community.