Historic residential buildings in Lingnan are affected by hot–humid and disaster-prone weather conditions, including high temperature, high humidity, intense solar radiation, monsoon winds, and typhoon-related climate stress, which challenge indoor thermal comfort, daylighting, natural ventilation, and adaptive reuse. Taking Jingzu Jiashu, a historic Chaoshan residence associated with overseas remittance culture, as a case study, this study develops a simulation workflow for climate-adaptive green retrofit. Digital documentation, architectural survey, material investigation, and climate data were integrated to establish a baseline model. PMV, DA300, and ACH/ACR were used to evaluate thermal comfort, daylighting, and natural ventilation. The baseline results show summer overheating, insufficient daylighting in deep rooms, and inadequate ventilation in representative rooms. Comfortable hours accounted for only 7.29–7.78%, thermally uncomfortable hours reached 42.84–51.53%, and the maximum PMV reached 4.65 in the rear hall and 3.54–3.65 in representative rooms. The effective daylight areas of the front and rear rooms were approximately 40% and 31%, while baseline ACH values ranged from 1.06 to 1.89 h⁻¹. An integrated retrofit strategy was proposed, including functional reorganization, envelope optimization, opening adjustment, ventilation-path organization, and courtyard/transitional-space improvement. After retrofit, comfortable hours increased to 32.00–42.45%, thermally uncomfortable hours decreased to 17.25–21.28%, maximum PMV values decreased to 1.82–1.86, daylight areas increased to 81% and 74%, and ACH values rose to 2.97–4.49 h⁻¹. The results indicate that building performance simulation can provide quantitative support for climate-adaptive green retrofit of historic Chaoshan residences in Lingnan, offering a methodological reference for healthier, lower-carbon, and more resilient reuse of similar historic dwellings.

The humid coastal Lingnan region of South China, including the Chaoshan area of eastern Guangdong, is frequently exposed to disaster-prone weather conditions such as high humidity, typhoon-related winds, heavy rainfall, and salt-laden coastal air. These long-term environmental exposures may contribute to surface deterioration risks of architectural heritage. Located in Shantou, Yingchuan Shijia has shown five visible surface deterioration types—cracks, staining, saltpetering, plants, and spalling—under the combined influence of environmental exposure, material aging, previous disturbance, and insufficient maintenance. To address the limitations of manual inspection, this study explores a conservation-oriented intelligent workflow integrating YOLO-based detection, digital documentation, and screening-level conservation interpretation. Digital documentation used UAV imagery, mobile LiDAR scanning, measured drawings, and SketchUp-based three-dimensional modeling. The dataset was built in three stages: a 99-image preliminary dataset, where YOLOv8 showed only basic learning capability with low performance metrics, including Precision of 33.0 ± 3.0%, Recall of 28.0 ± 1.0%, mAP50 of 25.0 ± 1.0%, and mAP50-95 of 11.0 ± 1.0%; a 362-image non-augmented case-study dataset, where YOLOv8 still showed limited performance, with mAP50 of 20.0 ± 1.0% and mAP50-95 of 8.0 ± 1.0%; and a final YOLO-format case-study dataset of 2000 images after training-set-only augmentation using 11 geometric and photometric transformation methods. After augmentation, YOLOv8 mAP50 increased to 62.0 ± 2.0%. Under the same augmented-data condition, YOLOv13 showed Precision of 89.0 ± 1.0%, Recall of 77.0 ± 1.0%, mAP50 of 84.0 ± 1.0%, and mAP50-95 of 65.0 ± 1.0%, indicating relatively higher validation performance than YOLOv8. In the normalized confusion matrix, the background missed-detection values for cracks and saltpetering were 0.29 and 0.22, respectively, indicating that weak-feature and low-contrast deterioration types remained challenging. Based on YOLOv13, a mini program was developed to organize detection outputs and provide field-oriented preliminary conservation hints. Overall, this study provides a preliminary workflow linking digital collection, image-based deterioration detection, Grad-CAM visualization, and assisted field recording for the preventive conservation of Chaoshan overseas-Chinese residences in humid coastal heritage environments.

Integrated qiaopi office–residence heritage preserves the material setting of remittance-letter operations together with domestic, educational, and ritual activities. In Lingnan’s hot–humid and disaster-prone environment, condition records need to be repeatable, spatially traceable, and continuously updatable. Taking Jingzu Jiashu and Mingde Jiashu, two former qiaopi office sites in Chaoshan, as case studies, this research develops an evidence-traceable digital conservation workflow integrating multi-source documentation; an adopted YOLOv8 surface-deterioration baseline; qualitative Grad-CAM visualization; structured deterioration records; and semi-automatic, human-confirmed Revit/HBIM association. UAV and terrestrial photography, mobile LiDAR/scanning, handheld measurement, measured drawings, point-cloud and reality-based products, and geometric models were organized into case-specific HBIM environments. The adopted deterioration dataset comprised 362 original images at 512 × 512 pixels and 2024 bounding-box annotations for five visually identifiable categories: spalling, staining, plants, saltpetering, and crack. The original images were divided into 253 training, 72 validation, and 37 independent-test images, while augmentation was restricted to the training subset, increasing the training pool to 1600 images. The previously established YOLOv8 baseline achieved a Precision of 0.85, Recall of 0.72, mAP50 of 0.83, and mAP50–95 of 0.58. Grad-CAM heatmaps were used as qualitative aids to examine model-emphasized image regions. Retained detections associated with Jingzu Jiashu and Mingde Jiashu were converted into versioned records containing source-image identifiers, deterioration classes, detector confidence, survey information, spatial references, verification states, and revision histories. Candidate spatial associations were generated through case identifiers, façade or space zones, element identifiers, and available spatial evidence, while final M1–M3 associations required human confirmation. By preserving source provenance, spatial uncertainty, and record histories, the workflow provides an auditable information basis for routine inspection, post-event review, maintenance prioritization, repair interpretation, and resilience-oriented preventive conservation. The workflow supports screening-level deterioration recognition and information management but does not provide causal diagnosis, structural assessment, exact affected-area measurement, building-independent generalization, or automatic repair recommendations.

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.