Learning Administrative Boundary-Aware Location Encoders via Reverse Geocoding

We use reverse-geocoded text alongside semantic IDs as self-supervised training signals so that location encoders learn administrative boundaries and place names. This improves existing encoder architectures, and our RevGeoRQ architecture outperforms contemporary location encoders on several downstream tasks. The human-readable reverse-geocoded strings and discrete semantic IDs also make the learned representations more interpretable.

Recommended citation: Lakshay Sharma, Alex Marin. (2026). "Learning Administrative Boundary-Aware Location Encoders via Reverse Geocoding." arXiv preprint.