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Digital street-view imagery delivers computationally enabled fine-grained quantitative perception of urban landscape, supporting the refined assessment of urban spatial quality, ecological livability, and socio-economic externalities for socio-environmental sustainability. Taking Shanghai as the research area and 500 m × 500 m grid cells as basic analytical units, this study adopts the Segformer machine learning segmentation algorithm to accurately extract visual features of urban green infrastructure from massive Baidu street-view images. Combined with visitor volume and real estate transaction data, this paper systematically explores vitality effects and sustainable economic compensation of Green View Index (GVI) via the spatial hedonic model. Multi-model comparison verifies that the double-logarithmic framework is optimal for street-view visual data. Two empirical models are constructed to eliminate multicollinearity and differentiate effects of integrated built environments and segmented visual elements. The results indicate that Shanghai’s vitality presents a polycentric agglomeration pattern, while GVI shows a scattered spatial distribution, with strong spatial correlation in urban cores and weak linkage in suburbs. GVI acts as a determinant of block vitality, outperforming land use diversity and commercial density. A 1% increase in the GVI improves block vitality by 6.21% in C gradient, with positive effects concentrated on outer-ring green belts and inhibitory impacts in remote suburbs. Multi-scale analysis from 500 m to 5000 m confirms green optimization brings stable residential premiums, generating sustainable economic compensation to offset urban renewal costs. This study proposes a digital-imagery-based paradigm for GVI benefit evaluation and provides empirical evidence for the planning of ecologically sustainable communities.
WANG et al. (Sun,) studied this question.