A team from Google Research and USC has released Mobility-Embedded POIs (ME-POIs), a framework that folds aggregate human movement into text-based place embeddings. The premise is that language models describe what a place is, but not how it is used. Two coffee shops can share a category, an address block, and a text vector, while one runs commuter turnover and the other holds customers for ninety minutes. ME-POIs encodes each visit as a contextualized vector, then uses contrastive learning to align those visits with one learnable prototype per POI. Across five map-enrichment tasks on Los Angeles and Houston mobility data, adding ME-POIs to strong text encoders improved 34 of 35 model-task pairings in Los Angeles, with relative gains up to 81.9% F1 on visit intent and a 24.7% MAE reduction on busyness. Notably, a variant trained on mobility alone beat Gemini embeddings on price-level classification.

Is it deployable?

Partially, it is a framework you rebuild, not a checkpoint you download. As of publication, Google Research has released the paper but no public code or weights. The compute bar is low: the model is ~53.7M parameters and was pretrained on a single NVIDIA Tesla V100 16GB. The real gate is data — you need licensed foot-traffic or first-party visit logs plus POI polygons.