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arXiv · 2506.16898

AI's Blind Spots: Geographic Knowledge and Diversity Deficit in Generated Urban Scenario

Ciro Beneduce·Massimiliano Luca·Bruno Lepri
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Abstract

Diffusion-based text-to-image models are increasingly used for urban analysis and scenario generation, but their geographic knowledge and representational biases remain poorly understood. We evaluate FLUX 1-schnell and Stable Diffusion 3.5-Large in the United States by generating 150 street-view images for each state, each state capital, and a generic "USA" prompt. Images are embedded with DINO-v2 ViT-S/14 and compared with Fr\'echet Inception Distance (FID). Pairwise FID clustering shows that geographically proximate states and capitals often group together, indicating implicit geographic structure. However, the generic ``USA'' prompt collapses this diversity into a metropolitan stereotype: frontier, desert, tropical, rural, and small-city environments are underrepresented or distant in FID space. These results show that diffusion models can encode fine-grained geography while still reproducing narrow national-scale visual stereotypes.

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Ciro Beneduce, Massimiliano Luca, Bruno Lepri. 2025-06-20. AI's Blind Spots: Geographic Knowledge and Diversity Deficit in Generated Urban Scenario. https://arxiv.org/abs/2506.16898

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