A Deep Learning-Based Decision-Support Framework for Assessing the Conservation Condition and Protection Priority of Huizhou Historic Buildings

Efficient conservation prioritization of Huizhou historic buildings requires joint consideration of visible deterioration and value-bearing architectural elements. This study developed a deep learning-based decision-support framework using an original field dataset of 500 facade images and 2197 audited annotations from five towns in Shexian County, China. Separate YOLOv11n and YOLOv11n-seg models detected six decorative or typological element classes and segmented cracks, spalling, and stains, respectively. Model outputs were converted into a pathology severity index (PSI) based on mask-pixel-area ratios and a decorative value index (DVI) based on weighted element counts; the two indices were combined into a conservation priority index (CPI), whose weighting was examined through sensitivity analysis. On the validation set, decorative-element detection yielded a mAP@50 of 0.655, and pathology segmentation yielded a mask mAP@50 of 0.586. In a preliminary application to four held-out buildings, model-derived priority categories matched the blind ratings of three conservation experts in three cases. The framework offers interpretable evidence for preliminary screening and resource-allocation discussions, but it does not replace field diagnosis. Larger balanced datasets, external building-level validation, and metric calibration are required before regional deployment.