Evaluating Urban Street Space Quality Using Multi-Source Data and Fully Convolutional Neural Networks: A Case Study of Xi’an’s Historic Urban Area

Street space quality in historic cities has become a central concern in heritage conservation and urban renewal. Nevertheless, existing evaluation frameworks often overlook the historical dimension and insufficiently address the interaction effects among influencing factors. Taking the historic urban area of Xi’an as a case study, this study constructed a comprehensive assessment framework for street space quality comprising five dimensions: accessibility, comfort, convenience, safety, and historicity. A total of 15 indicators were quantified for 404 street segments across four street typologies—commercial, residential, historic, and mixed-use—using multi-source data and Baidu Street View image analysis based on a fully convolutional neural network. The GeoDetector model was then applied to identify key influencing factors and explore their interaction effects. The results reveal that comfort and convenience are the dominant dimensions affecting street space quality. Among all indicators, street interface permeability and facility density show the strongest explanatory power. Furthermore, all pairs of influencing factors exhibit either bi-factor enhancement or nonlinear enhancement, highlighting the synergistic effects of multiple variables in shaping street quality. Based on these findings, this study proposes differentiated renewal strategies for the four street types and offers a transferable methodological framework for data-driven assessment and targeted intervention in the renewal of historic urban streets.