## 3 Data and Methodology
### Acquisition of Research Data  
(1) Wuchang District Street-View Image Data  
Streetscape images can provide a wide range of geographic coverage and images, so streetscape image acquisition and extraction are critical channels for the large-scale measurement of urban spatial research. Streetscape images reflect the most direct visual scene of human vision and thus have a wide range of application value in urban street-space research. We acquired the streetscape images of Wuchang District through the API interface 'Baidu Map'. These streetscape images correlated with the road network and provided geographic coordinate information. Eventually, we collected more than 26,000 POI points, covering most of the Wuchang District area. River areas on the map do not have streets, and since there are several water areas in Wuchang District, they therefore do not have collection points. The location distribution of the street-view data collection points is not equidistant, meaning some areas are densely populated with collection points. Still, POI points provide more complete coverage of the overall land area. Street-view shooting is mainly performed using a street-view shooting vehicle shooting along the road. We collected four street-view images according to the four relative angles of each point. In order to obtain street-view pictures of the street-view points to the front, back, left, and right of the four directions of the sampling point, the azimuth angle was set to be from 0 degrees to 360 degrees, and a 360-degree panoramic street-view was formed in the end. The storage attributes of the street-view images include the X coordinate, Y coordinate, and azimuth angle. After cleaning the images, they were projected to the corresponding geographic locations. (2) POI points for public facilities in Wuchang District  
The POI points for public facilities in Wuchang District were obtained through the POI interface of the Baidu map and retrieved through administrative divisions. To facilitate the subsequent research, the POI data were defined to include detailed fields such as the POI name, latitude, longitude, and exact address. The categories included food and beverage facilities, governmental facilities, medical facilities, leisure and recreation facilities, etc. Semantic Segmentation and Spatial Measurement Index Calculation of Street-View Images Based on Deep Learning  
The machine learning algorithm represented by PSPNet was constructed based on a deep convolutional neural network that can process street scene images in depth [52] and recognize various elements in an image, such as the sky, sidewalks, cars, buildings, etc. PSPNet stands for a pyramid scene parsing network, a scene-analyzing network constructed using the pyramid pooling module [53]. Based on deep learning methods, it performs a pixel-level segmentation of the semantics of objects such as the sky, roads, and plants appearing in an image [54]. We chose this model because PSPNet produced a new mIoU accuracy record of 85.4% on PASCAL VOC 2012 and 80.2% on Cityscapes [55]. This model uses a technique that enables a more accurate segmentation of scene image elements. The street-view imagery from more than 26,000 collection points in Wuchan was segmented. In this study, the method is mainly used to obtain the percentage of each visual element of the street. A total of 14 visual elements are required to calculate the five spatial measurement indicators, and the 14-element segmentation results are shown in Table 1.  
Combined with the segmentation results of street-view images, five spatial measures were selected in this study to quantify human visual elements. The application of green vision and sky visibility is very common in urban spatial measurements. Green space quality is often associated with urban health and has a certain impact on urban spatial perception [56]. Sky visibility is also important for the study of urban heat islands. At the same time, the sky represents infinity in space, which is also related to the visibility of the line of sight.