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摘要
The Sky View Factor (SVF) is one of the most important indicators to characterize urban radiation fluxes and urban thermal environment. Therefore, it is a key morphological parameter to study the Urban Heat Island (UHI) effect. Studies have shown that SVF has a strong relationship with UHI intensity. Nevertheless, the relationships found can be contradictory. This is primarily due to the fact that the cases studied are often in different regions with different climatic conditions. In addition, the influences of trees are sometimes ignored due to the lack of vegetation data or the limitation of calculating methods. How to calculate SVF quickly and accurately is important to urban climate research. SVF is typically calculated by four types of methods: fisheye photo methods, 3D GIS methods, GPS methods, and street view image methods. Compared with the other types of methods, calculating SVF using street view images has many advantages, such as widely available data, low cost, high efficiency, and the ability to consider the influences of trees and other obstacles. On the one hand, street view images provide the possibility for fast and accurate calculation of SVF in large-scale areas. On the other hand, the street view image method is still at its developing stage and more work needs to be done to verify its application in various urban environments. In this study, we proposed an automatic SVF calculation method using street view images and deep learning algorithms, and then applied the method to the UHI study in the city center of Shanghai. Baidu static panoramas and Deeplabv3+ were used to detect sky range while MATLAB code was written to calculate SVF. A Landsat-8 OLI / TIRS image was also used to retrieve land surface temperature at street level in the study area. Based on the Local Climate Zones (LCZ) scheme, we combined large-scale SVF value with the land use and building morphology to examine the relationship between SVF and UHI intensity. The results showed that Deeplabv3+ can detect the sky and non-sky range effectively in different scenarios (MIOU=91.64%). The SVF calculated using the proposed method was in good agreement with that calculated using fish-eye photos (R2=0.8869). The LCZ scheme provides new insights for the relationship between SVF and UHI. For LCZ5 and LCZ1, the highest correlation coefficients were 0.68 and -0.79, respectively. The proposed method was shown to be applicable in high-density and complex urban environments. In addition, the calculation of large-scale continuous SVF provides the possibility for zonal understandings of the UHI effect based on the LCZ scheme. 2021, Science Press. All right reserved.
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