GenAI Models Capture Urban Science but Oversimplify Complexity
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908570276069376 |
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| author | Zhang, Yecheng Zhao, Rong Huang, Zimu Wang, Xinyu Ma, Yue Long, Ying |
| author_facet | Zhang, Yecheng Zhao, Rong Huang, Zimu Wang, Xinyu Ma, Yue Long, Ying |
| contents | 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 that systematically evaluates leading GenAI models by testing their fidelity in generating both symbolic and perceptual urban data. 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 diversity, systematic parametric deviations, and 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_13803 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GenAI Models Capture Urban Science but Oversimplify Complexity Zhang, Yecheng Zhao, Rong Huang, Zimu Wang, Xinyu Ma, Yue Long, Ying Physics and Society I.6.4; K.4.0 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 that systematically evaluates leading GenAI models by testing their fidelity in generating both symbolic and perceptual urban data. 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 diversity, systematic parametric deviations, and 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. |
| title | GenAI Models Capture Urban Science but Oversimplify Complexity |
| topic | Physics and Society I.6.4; K.4.0 |
| url | https://arxiv.org/abs/2505.13803 |