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[2602.15750] UrbanVerse: Learning Urban Region Representation Across Cities and Tasks
Summary
UrbanVerse proposes a novel model for urban region representation learning that generalizes across cities and tasks, enhancing urban analytics capabilities.
Why It Matters
This research addresses the limitations of existing urban representation methods by introducing a model that improves cross-city and cross-task generalization. The implications for urban analytics are significant, as it can lead to better predictive models and insights for urban planning and management.
Key Takeaways
- UrbanVerse enhances urban region representation learning across different cities.
- It utilizes local and neighborhood features for improved generalization.
- The model includes a cross-task learning module for better task performance.
- Experiments show up to 35.89% improvement in prediction accuracy over existing methods.
- UrbanVerse can be integrated with other urban representation models.
Computer Science > Machine Learning arXiv:2602.15750 (cs) [Submitted on 17 Feb 2026] Title:UrbanVerse: Learning Urban Region Representation Across Cities and Tasks Authors:Fengze Sun, Egemen Tanin, Shanika Karunasekera, Zuqing Li, Flora D. Salim, Jianzhong Qi View a PDF of the paper titled UrbanVerse: Learning Urban Region Representation Across Cities and Tasks, by Fengze Sun and 5 other authors View PDF HTML (experimental) Abstract:Recent advances in urban region representation learning have enabled a wide range of applications in urban analytics, yet existing methods remain limited in their capabilities to generalize across cities and analytic tasks. We aim to generalize urban representation learning beyond city- and task-specific settings, towards a foundation-style model for urban analytics. To this end, we propose UrbanVerse, a model for cross-city urban representation learning and cross-task urban analytics. For cross-city generalization, UrbanVerse focuses on features local to the target regions and structural features of the nearby regions rather than the entire city. We model regions as nodes on a graph, which enables a random walk-based procedure to form "sequences of regions" that reflect both local and neighborhood structural features for urban region representation learning. For cross-task generalization, we propose a cross-task learning module named HCondDiffCT. This module integrates region-conditioned prior knowledge and task-conditioned semantics into the ...