Generative AI-Assisted Principal-Facade Renewal of Lingnan Coastal Vernacular Architectural Heritage in Yun’ao Town Under Hot-Humid and Disaster-Prone Weather Conditions: A Grasshopper–Pix2Pix Framework

Abstract

AI-assisted renewal of living vernacular settlements must preserve regional identity and historical layering while keeping professional judgment central; however, current facade-generation studies rarely connect field-derived architectural knowledge, controllable data construction, and subsequent design development within one verifiable workflow. Taking Yun’ao Town as a case study, this study develops a Grasshopper–Pix2Pix method for preliminary renewal of principal (front) facades—the primary public-facing elevations—in a hot-humid coastal setting exposed to disaster-prone weather, which is treated only as maintenance and renewal background rather than as a generative input, constraint, or performance metric. Field surveys documented 310 representative buildings and 1240 facade images. Typological and component analyses informed 160 parametric base models, which were expanded into 870 Facade Semantic Layout (FSL)–Building Elevation (BE) image pairs using four fixed parameters and eleven groups of linked parameters. The dataset was allocated at approximately 80%/10%/10% for training, validation, and testing at the base-model-group level to reduce information leakage, and Pix2Pix was trained for 1000 epochs. Across six representative cases, SSIM ranged from 0.33 to 0.65, with a descriptive mean of 0.54; these values do not represent aggregate validation- or test-set performance. Qualitative inspection identified distorted openings, incomplete balcony edges, interrupted roof lines, unclear railing relationships, ornamental discontinuity, and occasional component overlap. A mini-program and Huangang Road application examined how selected outputs could enter a human-supervised sequence of preliminary comparison, professional screening, manual correction, three-dimensional modeling, and technical drawing development. The contribution is a field-informed, human-supervised generative workflow rather than autonomous design; its outputs remain preliminary visual references requiring measured-survey, conservation, structural, environmental, material, and construction verification.