Combining deep learning and crowdsourcing geo-images to predict housing quality in rural China
This work addresses the need for detailed housing quality data in rural China to inform development policies, though it is incremental by applying existing methods to a new domain.
The researchers tackled the problem of predicting housing quality in rural China by combining deep learning with crowdsourced geo-images, achieving automated and efficient predictions at the village level.
Housing quality is an essential proxy for regional wealth, security and health. Understanding the distribution of housing quality is crucial for unveiling 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 unpack the housing quality at the village level. To fill the gap between accurately depicting rural housing quality conditions and deficient data,we collect massive rural images and invite users to assess their housing quality at scale. Furthermore, a deep learning framework is proposed to automatically and efficiently predict housing quality based on crowd-sourcing rural images.