📝 SummarXiv

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.