Table 1_Design of conditional control generation based on regional feature quantification: practical investigation of diffusion models in developed urban areas.xlsx

IntroductionAgainst the backdrop of rapid urbanization, urban form homogenization has become a prominent challenge, seriously eroding cities’ unique cultural identities and functional diversity. Traditional urban form design mainly relies on designers’ subjective experience, which often fails to strike a reasonable balance between the accurate representation of urban morphological attributes and the effective preservation of spatial diversity. To address this critical limitation, this study proposes a deep learning-based conditional generative control model, with metropolitan areas of developed cities around the world as the specific research context.
MethodsTo verify the effectiveness of the proposed model, we adopted a systematic research method: first, we built a 5-dimensional urban form evaluation framework (covering shape, scale, compactness, fragmentation, and proximity) based on landscape pattern indices; second, we constructed a high-quality urban morphology dataset; third, we verified the correlation between landscape pattern indices and generated urban form features; finally, we conducted comprehensive tests to evaluate the model’s accuracy and scalability.
ResultsThe experimental results show that in over 90% of the sample images, the Percentage of Like Adjacencies (PLADJ) index exhibited the strongest correlation with the urban morphological features generated by the model. Meanwhile, the proposed model achieved an average accuracy of 85.72% in generating urban morphological indicators across the five core dimensions, proving its good performance in simulating urban forms.
DiscussionThis study reveals that although single-dimensional indicators are context-dependent, they are closely correlated with the overall characteristics of urban form. The research innovatively integrates landscape pattern quantification with conditional generative models, providing a practical technical tool for urban planning. Its limitations lie in the narrow research context, and there is still room for optimization in indicator selection and model efficiency.