论文标题
结合深度学习和众包地理图像以预测中国农村的住房质量
Combining deep learning and crowdsourcing geo-images to predict housing quality in rural China
论文作者
论文摘要
住房质量是区域财富，安全和健康的重要代理。了解住房质量的分布对于揭示农村发展状况并提供政治建议至关重要。但是，目前的农村房屋质量数据在很大程度上取决于在国家或省级的自上而下的，耗时的调查，但未能在乡村层面解开住房质量。为了填补准确描绘农村住房质量条件和数据不足之间的空白，我们收集了大量的农村图像，并邀请用户按大规模评估其住房质量。此外，提出了一个深度学习框架，以根据众包农村图像自动有效地预测住房质量。
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.
