The Shantou Xiaogongyuan Historic District is a significant cultural symbol of the “Century-Old Commercial Port,” embodying the historical memory of the Chaoshan diaspora culture and modern trade. However, amid rapid urbanization, the area faces challenges such as the degradation of architectural façade styles, the erosion of historical features, and inefficiencies in traditional restoration methods, often resulting in renovated façades that exhibit “form resemblance but spirit divergence.” To address these issues, this study proposes a method integrating computer vision and generative design for historical building façade renewal. Focusing on the arcade buildings in the Xiaogongyuan District, an intelligent façade generation system was developed based on the pix2pix model, a type of Conditional Generative Adversarial Network (CGAN). A dataset of 200 annotated images was constructed from 200 field-collected façade samples, including Functional Semantic Labeling (FSL) diagrams and Building Elevation (BE) diagrams. After 800 training epochs, the model achieved stable convergence, with the generated schemes achieving compliance rates of 80% in style consistency, 60% in structural integrity, and 70% in authenticity. Additionally, a WeChat mini-program was developed, capable of generating façade drawings in an average of 3 s, significantly improving design efficiency. The generated elevations are highly compatible and can be directly imported into third-party modeling software for quick 3D visualization. In a practical application at the intersection of Shangping Road and Zhiping Road, the system generated design alternatives that balanced historical authenticity and modern functionality within hours, far surpassing the weeks required by traditional methods. This research establishes a reusable technical framework that quantifies traditional craftsmanship through artificial intelligence, offering a viable pathway for the cultural revitalization of the Xiaogongyuan District and a replicable systematic approach for AI-assisted renewal of historic urban areas.
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W. Yan
T Wang
Chunyan Zhang
Buildings
Shantou University
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Yan et al. (Fri,) studied this question.
synapsesocial.com/papers/6940224e2d562116f28fc023 — DOI: https://doi.org/10.3390/buildings15244404