View factors estimation from street view imagery and its correlation with intra-urban thermal variation

Abstract

High temperatures negatively impact thermal comfort and public health. Understanding the relationship between urban morphology and thermal variation is crucial for mitigating excessive heat. In this study, we propose a framework that uses the sky view factor (SVF), building view factor (BVF), and green view factor (GVF) to represent street-level spatial morphology, computed from Baidu street view images using semantic segmentation and fisheye transformation. The framework also integrates local climate zones (LCZs) classification with summer Landsat thermal data to explore the relationships between SVF, BVF, GVF, and intra-urban thermal variation. An empirical study is conducted in the core built-up area of Fuzhou. Key findings include: (1) Strong urban thermal intensity is characterized by high openness (SVF_mean = 0.60) and low vegetation (GVF_mean = 0.15). (2) In general, SVF (r = 0.27) is positively associated with street thermal intensity, whereas BVF (r = -0.15) and GVF (r = -0.21) exhibit negative correlations, based on the entire study area. (3) Within LCZ classes (e.g., LCZs 1–5), SVF (54.57% of the grids) is mostly positively correlated with temperature difference, while GVF (61.30% of the grids) is predominantly negatively correlated with temperature difference. (4) BVF shows the greatest uncertainty in its relationship with thermal variation, as it effectively quantifies exposure to heat-trapping building surfaces that dominate the local energy balance. The above findings imply that there is spatial heterogeneity in the relationship between view factors and intra-urban thermal variation. These results aid in developing street renewal strategies for urban heat mitigation.

Keywords

View factors / Urban thermal environment / Street view images (SVIs) / Semantic segmentation / Local climate zones (LCZs)

Declaration of competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

CRediT authorship contribution statement

Jiahui Chen: Writing – review & editing, Writing – original draft, Visualization, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Liyu Tang: Writing – review & editing, Project administration, Methodology, Investigation, Funding acquisition, Conceptualization. Simin Liu: Writing – review & editing, Conceptualization. Xueman Zuo: Writing – review & editing. Junqian Cao: Writing – review & editing.

Acknowledgements

This study was founded by the National Natural Science Foundation of China (Grant No. 42371482).

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