ABSTRACT Quantifying the synergistic effects of urban environmental features remains a challenge in geocomputation and urban modeling. Traditional studies often treat the Green View Index (GVI) and Sky View Factor (SVF) as independent variables, overlooking their complex interaction in determining environmental quality. This study proposes a hybrid computational framework coupling semantic segmentation with spatial statistics to construct the Street Environment Composite Metric (SECM) for property valuation. Utilizing the DeepLabV3+ model, we processed massive unstructured Baidu Street View (BSV) imagery to extract high‐precision semantic features. To enhance spatial modeling granularity and mitigate the Modifiable Areal Unit Problem (MAUP), we implemented a fine‐grained segmentation algorithm for street vectors. These computed metrics were then integrated into a Multiscale Geographically Weighted Regression (MGWR) model to reveal the spatial non‐stationarity of housing price determinants in Shanghai's Xuhui District. Computational results demonstrate that the SECM provides a superior fit for capturing the non‐linear interactions between greenery and spatial openness compared to single‐variable models. Specifically, the analysis reveals a threshold effect: Sky View Factor (SVF) contributes to economic value only when supported by a sufficient ecological foundation. In low‐GVI zones, the SECM effectively identifies environmental deficits that suppress property values. This study presents a reproducible workflow for integrating deep learning‐based feature extraction with advanced spatial econometrics, offering a robust tool for fine‐scale urban environmental assessment.
Meng et al. (Sun,) studied this question.