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Housing quality is an essential proxy for regional wealth, security andhealth. Understanding the distribution of housing quality is crucial forunveiling rural development status and providing political proposals.However,present rural house quality data highly depends on a top-down,time-consuming survey at the national or provincial level but fails to unpackthe housing quality at the village level. To fill the gap between accuratelydepicting rural housing quality conditions and deficient data,we collectmassive rural images and invite users to assess their housing quality at scale.Furthermore, a deep learning framework is proposed to automatically andefficiently predict housing quality based on crowd-sourcing rural images.

Combining deep learning and crowdsourcing geo-images to predict housing quality in rural China