A Dynamic Network and Representation LearningApproach for Quantifying Economic Growth fromSatellite Imagery
This addresses the lack of reliable economic data in developing regions, offering an unsupervised method to aid decision-makers without costly surveys.
The paper tackles the problem of quantifying economic growth and living standards in developing countries by proposing a dynamic network and representation learning approach using satellite nightlight imagery, achieving accurate predictions of spatial gross economic expenditures.
Quantifying the improvement in human living standard, as well as the city growth in developing countries, is a challenging problem due to the lack of reliable economic data. Therefore, there is a fundamental need for alternate, largely unsupervised, computational methods that can estimate the economic conditions in the developing regions. To this end, we propose a new network science- and representation learning-based approach that can quantify economic indicators and visualize the growth of various regions. More precisely, we first create a dynamic network drawn out of high-resolution nightlight satellite images. We then demonstrate that using representation learning to mine the resulting network, our proposed approach can accurately predict spatial gross economic expenditures over large regions. Our method, which requires only nightlight images and limited survey data, can capture city-growth, as well as how people's living standard is changing; this can ultimately facilitate the decision makers' understanding of growth without heavily relying on expensive and time-consuming surveys.