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

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

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Article number2067
JournalRemote Sensing
Volume13
Issue number11
DOIs
Publication statusPublished - 2021 Jun 1

Keywords

  • Arbitrary area representation
  • Attention-augmented CNN
  • Daytime satellite imagery
  • Fine-grained GDP estimation
  • Nightlight

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)

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