Understanding urban wealth perception using a hybrid dataset and ranking‐scoring framework

Abstract

Research on urban perception using street view images has developed rapidly. Global urban perception datasets commonly used in local urban perception research may be inaccurate because of the differences in the characteristics across cities. Building a new local urban perception dataset requires a practical and efficient solution to construct high‐availability datasets and a corresponding urban perception framework. In this study, we developed a simple and effective ranking‐scoring framework containing two stages. First, we constructed a hybrid dataset in a 1:1 ratio, based on the global and local datasets. Next, we built a ranking‐scoring network that uses deep learning models to obtain the features of images and a learning‐to‐rank approach to determine perceptual scores. To validate the effectiveness of our method, we examined the perception of urban wealth in Wuhan, China. Our framework showed high accuracy when the hybrid dataset was used. Furthermore, we analyzed the differences between the spatial distribution of the perception results of the hybrid model and the global model in the study area. In addition, we analyzed driving factors for wealth perception, such as visual elements and road types. The results verified that the hybrid dataset is more suitable for local urban perception. Our framework could assist researchers in conducting fast and accurate urban perception of local cities, using a hybrid dataset and ranking‐scoring, thus enabling urban planners to provide more consideration to users' perceptions during the planning process without dwelling upon additional burdens.