Classification of architectural styles in Chinese traditional settlements using remote sensing images and building facade pictures

The classification of Chinese traditional settlements (CTSs) is extremely important for their differentiated development and protection. The innovative double-branch classification model developed in this study comprehensively utilized the features of remote sensing (RS) images and building facade pictures (BFPs). This approach was able to overcome the limitations of previous methods that used only building facade images to classify settlements. First, the features of the roofs and walls were extracted using a double-branch structure, which consisted of an RS image branch and BFP branch. Then, a feature fusion module was designed to fuse the features of the roofs and walls. The precision, recall, and F1-score of the proposed model were improved by more than 4% compared with the classification model using only RS images or BFPs. The same three indexes of the proposed model were improved by more than 2% compared with other deep learning models. The results demonstrated that the proposed model performed well in the classification of architectural styles in CTSs.