Optimizing Semantic Segmentation of Street Views with SP-UNet for Comprehensive Street Quality Evaluation

Traditional street quality evaluations are often subjective and limited in scale, failing to capture the nuanced and dynamic aspects of urban environments. This paper presents a novel and data-driven approach for objective and comprehensive street quality evaluation using street view images and semantic segmentation. The proposed SP-UNet (Spatial Pyramid UNet) is a multi-scale segmentation model that leverages the power of VGG16, SimSPPF (Simultaneous Spatial and Channel Pyramid Pooling), and MLCA (Multi-Level Context Attention) attention mechanisms. This integration effectively enhances feature extraction, context aggregation, and detail preservation. The model’s average intersection over union, Mean Pixel Accuracy, and overall accuracy achieving improvements of 5.83%, 6.52%, and 2.37% in mIoU, Mean Pixel Accuracy (mPA), and overall accuracy, respectively. Further analysis using the CRITIC method highlights the model’s strengths in various street quality dimensions across different urban areas. The SP-UNet model not only improves the accuracy of street quality evaluation but also offers valuable insights for urban managers to enhance the livability and functionality of urban environments.