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In the past decade, using Street View images and machine learning to measurehuman perception has become a mainstream research approach in urban science.However, this approach using only image-shallow information makes it difficultto comprehensively understand the deep semantic features of human perception ofa scene. In this study, we proposed a new framework based on a pre-trainnatural language model to understand the relationship between human perceptionand the sense of a scene. Firstly, Place Pulse 2.0 was used as our basedataset, which contains a variety of human-perceived labels, namely, beautiful,safe, wealthy, depressing, boring, and lively. An image captioning network wasused to extract the description information of each street view image.Secondly, a pre-trained BERT model was finetuning and added a regressionfunction for six human perceptual dimensions. Furthermore, we compared theperformance of five traditional regression methods with our approach andconducted a migration experiment in Hong Kong. Our results show that humanperception scoring by deep semantic features performed better than previousstudies by machine learning methods with shallow features. The use of deepscene semantic features provides new ideas for subsequent human perceptionresearch, as well as better explanatory power in the face of spatialheterogeneity.

A natural language processing-based approach: mapping human perception by understanding deep semantic features in street view images