An Assessment of the Urban Streetscape Using Multiscale Data and Semantic Segmentation in Jinan Old City, China

Abstract

Urban street space is a significant component of urban public spaces and an important aspect of people’s perceptions of a city. Jinan Old City exemplifies the balance between the supply of and demand for green spaces in urban streets. The sense of comfort and the demand level of street spaces are measured via the space demand index. Open platform data, such as those from Baidu Maps and Amap, are evaluated using methods including ArcGIS network analysis and Segnet semantic segmentation. The results obtained from such evaluations indicate that, in terms of the green space supply, the overall level for Shangxin Street in Jinan is not high. Only 24% of the selected sites have an adequate green space supply. The level on Wenhua West Road is higher than that on Shangxin Street. The block on the western side of Shangxin Street has the highest green space demand, with a decreasing trend from west to east. There are several higher selection points in the middle section of Shangxin Street. The demand is lowest in the middle of Wenhua East Road. Shangxin Street’s demand is higher than that of Wenhua West Road. The supply and demand are highly matched on Wenhua West Road and poorly matched on Shangxin Street, with 44.12% of the area in the “low supply, high demand” quadrant. This study proposes targeted optimization strategies based on supply and demand, thereby providing research ideas and methods for urban renewal.