Convolutional neural network for predicting poverty using satellite imagery
Abstract
Eradication of poverty in all its forms worldwide by 2030 is the rst of 17 Sustainable
Development Goals outlined by the United Nations. In Africa, where the bulk of
the world's poorest people live, national household surveys are used to collect data
on poverty. It is di cult to obtain accurate and timely information due to the
signi cant resource costs associated with conducting such surveys. Public access
to abundant data sources such as daytime satellite images and nighttime lights in
conjunction with advances in computer vision provides a feasible solution to the
data scarcity problem. The purpose of this research is to follow previous research
works in utilising machine learning techniques to process daytime satellite images
and nighttime lights in order to predict the distribution of poverty at the village level
in three African countries using more advanced technologies and up-to-date data.
The procedure involves designing and training a Convolutional Neural Network,
which is then used to extract poverty indicators from daytime satellite images. The
extracted features are mapped to poverty statistics and used in a regression model to
estimate poverty. In the initial nighttime light prediction task, the model attained
an accuracy of 75:29% on the training data and 77:97% on the validation data over
81,000 iterations. In the target task of poverty estimation, the model explained 18%
to 43% of the variation in average household expenditures in each country. The
results demonstrate that the transfer learning technique is generally applicable to
forecasting poverty in other countries, although its e ectiveness is highly dependent
on the hyperparameters used to tune the algorithm. Additionally, the technique does
not easily transfer to other poverty indicators such as child mortality and levels of
education.
Publisher
University of Nairobi
Rights
Attribution-NonCommercial-NoDerivs 3.0 United StatesUsage Rights
http://creativecommons.org/licenses/by-nc-nd/3.0/us/Collections
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