PlaceFM: A Training-free Geospatial Foundation Model of Places using Large-Scale Point of Interest Data

• URL: http://arxiv.org/abs/2507.02921v2
• Date: Thu, 02 Oct 2025 13:01:06 GMT
• Title: PlaceFM: A Training-free Geospatial Foundation Model of Places using Large-Scale Point of Interest Data
• Authors: Mohammad Hashemi, Hossein Amiri, Andreas Zufle,
• Abstract summary: PlaceFM captures place representations through a training-free, clustering-based approach.<n>placeFM summarizes the entire point of interest graph constructed from U.S. Foursquare data.<n>placeFM produces general-purpose region embeddings while automatically identifying places of interest.<n>placeFM achieves up to a 100x speedup in generating region-level representations on large-scale POI graphs.
• Score: 0.5735035463793009
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the rapid growth and continual updates of geospatial data from diverse sources, geospatial foundation model pre-training for urban representation learning has emerged as a key research direction for advancing data-driven urban planning. Spatial structure is fundamental to effective geospatial intelligence systems; however, existing foundation models often lack the flexibility to reason about places, context-rich regions spanning multiple spatial granularities that may consist of many spatially and semantically related points of interest. To address this gap, we propose PlaceFM, a geospatial foundation model that captures place representations through a training-free, clustering-based approach. PlaceFM summarizes the entire point of interest graph constructed from U.S. Foursquare data, producing general-purpose region embeddings while automatically identifying places of interest. These embeddings can be directly integrated into geolocation data pipelines to support a variety of urban downstream tasks. Without the need for costly pre-training, PlaceFM provides a scalable and efficient solution for multi-granular geospatial analysis. Extensive experiments on two real-world prediction tasks, ZIP code-level population density and housing prices, demonstrate that PlaceFM not only outperforms most state-of-the-art graph-based geospatial foundation models but also achieves up to a 100x speedup in generating region-level representations on large-scale POI graphs. The implementation is available at https://github.com/mohammadhashemii/PlaceFM.

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