Combining Deep Learning and Crowd-sourcing Images to Predict Housing Quality in Rural China

Abstract

Abstract Housing quality is an essential contributor to human well-being, security and health. Monitoring the housing quality is crucial for unveiling socio-economic development status and providing political proposals. However, it is exceedingly scarce to depict the nationwide housing quality in large-scale and fine-granularity in remote rural areas owing to the high cost of canonical survey methods. Taking rural China as an example, we collect massive rural house images for housing quality assessment by various volunteers and further build up a deep learning model based on the assessed images to realize an automatic prediction for huge raw house images. As a result, the model performance achieves a high R2 of 0.76. Afterward, the housing qualities of 10,000 Chinese villages are predicted based on 50,000 unlabeled geo-images, and an apparent spatial heterogeneity is uncovered. Specifically, divided by Qinling Mountains-Huaihe River Line, housing quality in southern China is much higher than in northern China. Our method provides high-resolution estimates of housing quality across the extensive rural area, which could be a complementary tool for automatically monitoring housing change and supporting house-related policy-making.