PlaceFM: A Training-free Geospatial Foundation Model of Places

• URL: http://arxiv.org/abs/2507.02921v1
• Date: Wed, 25 Jun 2025 15:10:31 GMT
• Title: PlaceFM: A Training-free Geospatial Foundation Model of Places
• Authors: Mohammad Hashemi, Hossein Amiri, Andreas Zufle,
• Abstract summary: We propose PlaceFM, a spatial foundation model that captures place representations using a training-free graph condensation method.<n>PlaceFM condenses a nationwide POI graph built from integrated Foursquare and OpenStreetMap data in the U.S., generating general-purpose embeddings of places.
• Score: 0.27309692684728604
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Spatial structure is central to effective geospatial intelligence systems. While foundation models have shown promise, they often lack the flexibility to reason about places, which are context-rich regions spanning different spatial granularities. We propose PlaceFM, a spatial foundation model that captures place representations using a training-free graph condensation method. PlaceFM condenses a nationwide POI graph built from integrated Foursquare and OpenStreetMap data in the U.S., generating general-purpose embeddings of places. These embeddings can be seamlessly integrated into geolocation data pipelines to support a wide range of downstream tasks. Without requiring pretraining, PlaceFM offers a scalable and adaptable solution for multi-scale geospatial analysis.

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