Formal Feature Identification of Vernacular Architecture Based on Deep Learning-A Case Study of Jiangsu Province, China

As an important sustainable architecture, vernacular architecture plays a significant role in influencing both regional architecture and contemporary architecture. Vernacular architecture is the traditional and natural way of building that involves necessary changes and continuous adjustments. The formal characteristics of vernacular architecture are accumulated in the process of sustainable development. However, most of the research methods on vernacular architecture and its formal features are mainly based on qualitative analysis. It is therefore necessary to complement this with scientific and quantitative means. Based on the object detection technique, this paper proposes a quantitative model that can effectively recognize and detect the formal features of architecture. First, the Chinese traditional architecture image dataset (CTAID) is constructed, and the model is trained. Each image has the formal features of "deep eave", "zheng wen", "gable" and "long window" marked by experts. Then, to accurately identify the formal features of vernacular architecture in Jiangsu Province, the Jiangsu traditional vernacular architecture image dataset (JTVAID) is created as the object dataset. This dataset contains images of vernacular architecture from three different regions: northern, central, and southern Jiangsu. After that, the object dataset is used to predict the architectural characteristics of different regions in Jiangsu Province. Combined with the test results, it can be seen that there are differences in the architectural characteristics of the northern, middle, and southern Jiangsu. Among them, the "deep eave", "zheng wen", "gable", and "long window" features of the vernacular architecture in southern Jiangsu are very outstanding. Compared with middle Jiangsu, northern Jiangsu has obvious features of "zheng wen" and "gable", with recognition rates of 45.8% and 27.5%, respectively. The features of "deep eave" and "long windows" are more prominent in middle Jiangsu, with recognition rates of 50.9% and 73.5%, respectively. In addition, architectural images of contemporary vernacular architecture practice projects in the Jiangsu region are selected and they are inputted into the AOD R-CNN model proposed in this paper. The results obtained can effectively identify the feature style of Jiangsu vernacular architecture. The deep-learning-based approach proposed in this study can be used to identify vernacular architecture form features. It can also be used as an effective method for assessing territorial features in the sustainable development of vernacular architecture.