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

TL;DR

This paper introduces a combined approach using deep learning and crowdsourcing to accurately assess rural housing quality in China, addressing data limitations of traditional surveys at the village level.

Contribution

It presents a novel framework that leverages crowd-sourced images and deep learning to efficiently predict rural housing quality, filling a significant data gap.

Findings

Effective prediction of housing quality from rural images

Scalable method for village-level housing assessment

Improved accuracy over traditional survey methods

Abstract

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.