Reconstructing Three-decade Global Fine-Grained Nighttime Light Observations by a New Super-Resolution Framework

TL;DR

This paper introduces a novel super-resolution framework that reconstructs high-resolution nighttime light data from low-resolution satellite images, enabling detailed long-term analysis of human activities worldwide.

Contribution

The authors developed a new super-resolution model that significantly improves the reconstruction of nighttime light data at global, national, and urban scales, surpassing existing models.

Findings

Correlation coefficient of 0.873 at global scale

Outperforms existing models in accuracy

Reveals historical development of urban facilities

Abstract

Satellite-collected nighttime light provides a unique perspective on human activities, including urbanization, population growth, and epidemics. Yet, long-term and fine-grained nighttime light observations are lacking, leaving the analysis and applications of decades of light changes in urban facilities undeveloped. To fill this gap, we developed an innovative framework and used it to design a new super-resolution model that reconstructs low-resolution nighttime light data into high resolution. The validation of one billion data points shows that the correlation coefficient of our model at the global scale reaches 0.873, which is significantly higher than that of other existing models (maximum = 0.713). Our model also outperforms existing models at the national and urban scales. Furthermore, through an inspection of airports and roads, only our model's image details can reveal the historical development of these facilities. We provide the long-term and fine-grained nighttime light observations to promote research on human activities. The dataset is available at \url{https://doi.org/10.5281/zenodo.7859205}.