Predicting the Construction Age of Vernacular Buildings from Facade Features Using Machine Learning

Determining the construction age of vernacular buildings is essential for the conservation and documentation of historical and cultural heritage. Traditional approaches, however, rely heavily on questionnaire surveys and expert judgment, which can be time-consuming and may be affected by incomplete historical information and variability in expert interpretation. To reduce reliance on direct expert assessment of construction age and improve the reproducibility of the dating process, this paper presents a machine-learning-assisted method for predicting construction age from manually coded facade features derived from building images. First, we visited 29 villages in the Dezhou region of China and compiled 630 vernacular building cases, creating a facade image dataset that spans multiple periods and architectural styles, with construction ages labeled as time intervals; human observers then coded 14 facade attributes for each building before model training. Random forest and decision tree models were then introduced to identify the core factors influencing age determination from a wide range of facade features and to establish their quantitative criteria. The results reveal that wall finishing materials, the presence of sunrooms, window materials, and wall body materials are the core factors affecting the judgment of construction age. Based on these factors, a decision tree model was constructed for age determination. This model achieved an accuracy of 97.62% on the test set, with both precision and recall exceeding 97% and an F1 score of 0.975, demonstrating the effectiveness and robustness of the proposed quantitative classification system. Within the Dezhou study setting and the coded time intervals, the method offers an interpretable and accurate technical pathway for supporting the dating of vernacular architectural heritage.