GenAI Models Capture Urban Science but Oversimplify Complexity

Generative artificial intelligence(GenAI) models are increasingly used for scientific data generation, yet their alignment with empirical knowledge in urban science remains unclear. Here, we introduce AI4US (Artificial Intelligence for Urban Science), a framework to systematically generate, evaluate, and calibrate urban data from leading GenAI models, testing their fidelity across both symbolic and perceptual domains. For the symbolic domain, we benchmark generated data against foundational urban theories concerning scale, space, and morphology. For the perceptual domain, we validate the models' visual judgments against human benchmarks and, critically, leverage their generative control to conduct in causal experiments on urban perception. Our findings show that while GenAI models reproduce core theoretical patterns, the generated data exhibit crucial limitations: poor diversityand systematic parametric deviations, and limited improvement from prompt engineering. To address this, we introduce a post-hoc calibration procedure using optimal transport, which produces synthetic symbolic datasets with demonstrably higher fidelity.