Nightlight as a proxy of economic indicators: Fine-grained gdp inference around chinese mainland via attention-augmented cnn from daytime satellite imagery

Haoyu Liu, Xianwen He, Yanbing Bai, Xing Liu, Yilin Wu, Yanyun Zhao, Hanfang Yang

Abstract:

The official method of collecting county-level GDP values in the Chinese Mainland relies
mainly on administrative reporting data and suffers from high costs of time, money, and human labor.
To date, a series of studies have been conducted to generate fine-grained maps of socioeconomic
indicators from the easily accessed remote sensing data and achieved satisfactory results. This paper
proposes a transfer learning framework that regards nightlight intensities as a proxy of economic
activity degrees to estimate county-level GDP around the Chinese Mainland. In the framework,
paired daytime satellite images and nightlight intensity levels were applied to train a VGG-16
architecture, and the output features at a specific layer, after dimensional reduction and statistics
calculation, were fed into a simple regressor to estimate county-level GDP. We trained the model
with data of 2017 and utilized it to predict county-level GDP of 2018, achieving an R-squared of
0.71. Furthermore, the results of gradient visualization confirmed the validity of the proposed
framework qualitatively. To the best of our knowledge, this is the first time that county-level GDP
values around the Chinese Mainland have been estimated from both daytime and nighttime remote
sensing data relying on attention-augmented CNN. We believe that our work will shed light on both
the evolution of fine-grained socioeconomic surveys and the application of remote sensing data in
economic research.