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To overcome the limited accuracy of existing street-vitality assessments under dense occlusion and their lack of dynamic, multi-source data fusion, this study proposes an integrated dynamic model that couples an enhanced YOLOv11 with heterogeneous spatio-temporal datasets. The network introduces a Two-backbone architecture for stronger multi-scale fusion, Spatial Pyramid Depth Convolution (SPDConv) for richer urban-scene features, and Dynamic Sparse Sampling (DySample) for robust occlusion handling. Validated in Yibin, the model achieves 90.4 % precision, 67.3 % recall and 77.2 % mAP@50—gains of 6.5 %, 5.3 % and 5.1 % over baseline. By fusing Baidu heat maps, street-view imagery, road networks and POI data, a spatial-coupling framework quantifies the interplay between commercial facilities and street vitality, enabling real-time diagnosis of urban dynamics, targeted retail regulation and adaptive traffic management. The work shifts urban resource allocation from static planning to dynamic, responsive systems.