Imagining the city: Evaluating visual realism and identity in AI-generated cityscapes using DALL·E 2

Despite rapid advances in Generative Artificial Intelligence (GenAI) and growing interest in its use for urban planning and design, its ability to authentically represent urban environments remains poorly understood. Most existing studies rely on online materials or expert evaluations, overlooking human-centered approaches that draw on the nuanced, place-based insights of local residents. This study aims to systematically evaluate the performance of OpenAI's DALL·E 2 in conveying both visual realism and identity in urban contexts. We conducted an online survey of 129 respondents, who assessed AI-generated images of one small town and three large cities in Virginia, each emphasizing one of Kevin Lynch's five urban elements. For each image, we quantified an AI-generated photo alignment score capturing two interrelated dimensions: visual realism and representational fidelity to the city's visual identity. Results show that images of landmarks received lower alignment scores than those of other elements, such as districts and edges, indicating GenAI's limitations in capturing culturally and historically significant urban features. Longer-term residents provided more critical evaluations, reflecting the role of place-based knowledge in shaping perceptions of AI imagery. Additionally, larger metropolitan areas received higher alignment scores than the small town of Blacksburg, revealing an uneven geography in GenAI's capabilities. This study delivers one of the first systematic, human-centered evaluations of AI-generated urban imagery, introducing a novel methodological framework that integrates local knowledge with place-based evaluation. The findings highlight critical ethical considerations for applying GenAI in urban planning and design, particularly in contexts where equitable and authentic representation is essential.