Deep Learning-Based Type Recognition and Spatial Analysis of Overseas Chinese Yanglou Dwellings in Jinjiang, China

Overseas Chinese Yanglou dwellings in Southern Fujian are important architectural heritage combining Western architectural elements and local construction traditions. However, their diverse facade forms and incomplete records make manual classification inefficient and difficult to quantify. Taking Jinjiang, China, as the study area, this research constructs a Yanglou facade image dataset and proposes a deep learning-based workflow for type recognition, model interpretation, and spatial analysis. Deep visual features and unsupervised clustering were used to identify three morphological patterns: traditional continuity, partial addition, and overall transformation. These patterns were further organized into a classification system of three categories and seven subtypes. YOLOv8n-cls, YOLO11n-cls, and YOLO26n-cls were compared for facade recognition, while feature map visualization, Grad-CAM, and t-SNE were used for model interpretation. GIS analysis suggests that traditional-continuity types are mainly distributed in inland plain and piedmont settlements. The Five-Foot Way type is widely distributed, whereas overall-transformation types are concentrated in several coastal or near-coastal villages. YOLO11n-cls achieved the best overall validation performance within the current Jinjiang sample set. The proposed workflow supports quantitative classification, interpretable recognition, and spatial documentation of Yanglou dwellings.