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 diffusion process to jointly model multiple downstream urban prediction tasks. HCondDiffCT is generic. It can also be integrated with existing urban representation learning models to enhance their downstream task effectiveness. Experiments on real-world datasets show that UrbanVerse consistently outperforms state-of-the-art methods across six tasks under cross-city settings, achieving up to 35.89% improvements in prediction accuracy.

翻译：近年来，城区表征学习的发展推动了城市分析领域的广泛应用，然而现有方法在跨城市与跨分析任务的泛化能力方面仍存在局限。本文旨在将城区表征学习推广至超越单一城市与单一任务的场景，构建面向城市分析的基础式模型。为此，我们提出UrbanVerse——一个面向跨城市城区表征学习与跨任务城市分析的模型。针对跨城市泛化，UrbanVerse聚焦于目标区域的局部特征及其邻近区域的结构特征，而非整个城市的全局信息。我们将区域建模为图节点，通过基于随机游走的流程构建“区域序列”，该序列同时涵盖局部特征与邻域结构特征，以支持城区表征学习。针对跨任务泛化，我们提出名为HCondDiffCT的跨任务学习模块。该模块将区域条件先验知识与任务条件语义整合至扩散过程中，以联合建模多个下游城市预测任务。HCondDiffCT具有通用性，亦可集成至现有城区表征学习模型中，提升其下游任务性能。在真实数据集上的实验表明，UrbanVerse在跨城市设定下的六项任务中均持续优于现有最优方法，预测精度最高提升达35.89%。