Perceiving rural China via deep learning, from street views to countryside.

Title: From Street View Imagery to the Countryside: Large-Scale Perception of Rural China Using Deep Learning

Perceiving rural China via deep learning, from street views to countryside

Abstract

Evaluating residents’ subjective perceptions of the rural environment is crucial for formulating effective rural planning. Due to the difficulty in obtaining rural data and the limitations of traditional questionnaire survey methods, existing research mostly focuses on small-scale perception evaluations in specific areas, making it difficult to reveal rural perception characteristics at the regional scale. To address this issue, our study proposes a rural living environment perception evaluation model based on street view images and deep neural networks, achieving a quantitative evaluation of rural perception in 118 cities nationwide. We collected a large data set of rural street view images nationwide through crowdsourcing and established an index system comprising five subjective perception dimensions: wealthy, tidy, lively, habitable, and terroir. By training a multidimensional quantitative evaluation model, we comprehensively evaluated residents’ subjective perceptions of China’s rural environment. Furthermore, we explored the relationship between these subjective perceptions and objective socioeconomic indicators. The model achieves an average evaluation accuracy of 75 percent across five dimensions, with the wealthy dimension exceeding 80 percent. Rural environment perception is comprehensively influenced by various factors such as economic base and traditional feature protection, showing significant differences between different regions. The perception of rural environment in the eastern region is closely related to economic levels, whereas perception in the western region is more affected by infrastructure improvement and social development. Overall, this study provides scientific evidence for formulating more targeted and effective rural planning.