Understanding the Visual Relationship between Function and Facade in Historic Buildings Using Deep Learning—A Case Study of the Chinese Eastern Railway

Although functional identifiability represents a key aspect for promoting visual connotation and sustainable usability in historic building groups, there is still no consensus on how to quantitatively describe its identification basis at a large scale. The recent emergence of the potentiality of deep learning and computer vision has provided an alternative to traditional empirical-based judgment, which is limited by its subjective bias and high traversal costs. To address these challenges, this study aims to build a workflow for a visual analysis of function and facade to extract the different contributions that facade elements provide to functional expression. The approach is demonstrated with an experiment on a section of the Chinese Eastern Railway (CER) where large-scale historical buildings images were categorized to identify functions using deep learning, together with activation and substance for visual calculations. First, the dataset aggregated with images of historic buildings along the CER was used to identify functional categories using SE-DenseNet merging channel attention. The results of the model visualized using t-SNE and Grad-CAM were then used to analyze the relationships of facade features across functional categories and differences in elemental feature representation across functional prototypes. The results show the following: (1) SE-Densenet can more efficiently identify building functions from the closely linked facade images of historic building groups, with the average accuracy reaching 85.84%. (2) Urban–rural differences exist not only in the count of spatial distributions among the CER’s historic building groups, but also in a significant visual divergence between functions related to urban life and those involved in the military, industry, and railways. (3) Windows and walls occupy areas with more characteristics, but their decorative elements have a higher intensity of features. The findings could enhance the objective understanding and deeper characteristics of the historical building group system, contributing to integrated conservation and characteristic sustainability.