The main challenge for approving and implementing policies aiming at sustainable development of country is correct prediction of socioeconomic condition. Deep learning algorithms in recent researches have been identified as potential resource to be applied in this domain. Another challenge is availability of sufficient amount of data which is solved using transfer learning in Convolutional Neural Network (CNN). We used pre-trained Inception Net-v3 and Ridge regression model to estimate poverty level using publicly available dataset comprising of daylight images, nightlight images and survey data. Each cluster of samples contains households between 1- 28. Its mean is 21.09, median 21 and a standard deviation is 1.36. Proposed deep learning inspired model estimates wealth-score for 28393 clusters with an r value i.e. Pearson Correlation Coefficient of 0.73, signifying r 2 value i.e. Coefficient of determination of 0.54. It shows that daytime satellite images, nightlight intensity and demographic data available can be utilized for precise evaluations about the spatial scattering of monetary prosperity crosswise over different nations.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.