LAUSR

The social-economic statistics collected from local governments are the main access for the central government to achieve national economic circumstance, especially for China. However, the statistics of almost 10% of national counties are missing or inconsistent due to the statistical caliber change in the wave of urbanization during economic development. Some researchers proposed to apply a night luminosity product to solve such issue. However, it lacks the ability to distinguish between the wealthy populations with a dense distribution and the less developed places. In this paper, the publicly available daytime Landsat images are used to estimate economic statistics. An end-to-end multi-task deep learning framework is constructed to estimate the county-level economy of Mainland China and the overall accuracy of this model achieves higher than 85%. The experiments show that our model provides a potential strategy to make up for the missing statistics and examines the reliability of the statistics collected for the central government.