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