UrbanFM: Scaling Urban Spatio-Temporal Foundation Models
Wei Chen, Yuqian Wu, Junle Chen, Xiaofang Zhou, Yuxuan Liang

TL;DR
UrbanFM introduces a scalable approach to modeling urban spatio-temporal data, leveraging large-scale datasets, novel computational units, and a minimalist architecture to improve generalization across cities and tasks.
Contribution
The paper systematically investigates scaling principles for urban spatio-temporal models, proposing new datasets, units, and architecture to enhance generalization and robustness.
Findings
UrbanFM achieves strong zero-shot transfer to unseen cities and tasks.
WorldST standardizes diverse urban signals into a billion-scale dataset.
MiniST enables unified representation of grid and sensor data.
Abstract
Urban systems, as dynamic complex systems, continuously generate spatio-temporal data streams that encode the fundamental laws of human mobility and city evolution. While AI for Science has witnessed the transformative power of foundation models in disciplines like genomics and meteorology, urban computing remains fragmented due to "scenario-specific" models, which are overfitted to specific regions or tasks, hindering their generalizability. To bridge this gap and advance spatio-temporal foundation models for urban systems, we adopt scaling as the central perspective and systematically investigate two key questions: what to scale and how to scale. Grounded in first-principles analysis, we identify three critical dimensions: heterogeneity, correlation, and dynamics, aligning these principles with the fundamental scientific properties of urban spatio-temporal data. Specifically, to…
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Taxonomy
TopicsHuman Mobility and Location-Based Analysis · Traffic Prediction and Management Techniques · Smart Cities and Technologies
