ABSTRACT
This study aimed to evaluate the performance of three artificial intelligence (AI) image synthesis models, Dall-E 2, Stable Diffusion, and Midjourney, in generating urban design imagery based on scene descriptions. A total of 240 images were generated and evaluated by two independent professional evaluators using an adapted sensibleness and specificity average metric. The results showed significant differences between the three AI models, as well as differing scores across urban scenes, suggesting that some projects and design elements may be more challenging for AI art generators to represent visually. Analysis of individual design elements showed high accuracy in common features like skyscrapers and lawns, but less frequency in depicting unique elements such as sculptures and transit stops. AI-generated urban designs have potential applications in the early stages of exploration when rapid ideation and visual brainstorming are key. Future research could broaden the style range and include more diverse evaluative metrics. The study aims to guide the development of AI models for more nuanced and inclusive urban design applications, enhancing tools for architects and urban planners.
