A fast approach for large-scale Sky View Factor estimation using street view images

Sky View Factor (SVF) is one of the most useful urban spatial indicators for radiation and thermal environmental assessment. Estimating SVF with circular fish-eye photos is straightforward and convenient and can account for obstruction of vegetation and other urban infrastructures. But for a large area with many points of interest, processing fish-eye photos is labor intensive. This paper presents a workflow of estimating SVF with large amounts of street view images obtained at sampling points along city road network at the height of about 2 m. To automatically estimate SVF with street view images, a batch processing sky region detection and SVF calculation tool was developed with the Python programming language and OpenCV. The tool can deal with various outdoor weather conditions, and the performance of sky region segmentation and SVF calculation was validated with photos taken with a fish-eye lens. The method shows reliable estimations and preferable speed, with about 1.5 s for a 1000 x 500 px image and 0.08 s for a 200 x 100 px image. The proposed workflow was further applied to estimate the SVF distributions in the downtown centers of four densely populated Chinese cities.