RE-SAT: Spatial-Aware Transformers with Semantic-Guided Prompting for Urban Region Embedding

Learning transferable region embeddings is a fundamental problem in urban computing, as such representations support a wide range of downstream prediction tasks. Existing methods leverage multi-view and multimodal urban data but often fail to explicitly model spatial relations across views or effectively align general region embeddings with task-specific objectives. In this paper, we propose a spatial-aware Transformer (RE-SAT) network with semantic-guided prompting for urban region embedding. RE-SAT adopts a two-stage learning paradigm. In the first stage, a spatial-aware Transformer encoder injects connectivity and distance-based spatial priors into the attention mechanism to learn task-agnostic region embeddings from multi-view urban data. In the second stage, RE-SAT adapts the learned embeddings to downstream tasks via a semantic-guided prompt learning mechanism, which generates task-aware soft prompts from textual task descriptions without modifying the universal embeddings. Extensive experiments on multiple urban prediction tasks across different cities demonstrate that RE-SAT consistently outperforms state-of-the-art methods, achieving maximum relative improvements of 12.2% in MAE, 12.2% in RMSE, and 6.7% in R2, validating its effectiveness and generalizability. Consequently, this framework serves as a robust decision-support tool for urban planners and policymakers, facilitating efficient resource allocation and intelligent city management across diverse scenarios.