| Summary: | The most exigent call of the United Nations’ 17 sustainable goals is to end poverty everywhere by 2030. Unlike in the past, when poverty was measured based on data collected through ground-level surveys, the new technology adopted by many developing and developed countries is to estimate the poverty index using remote sensing satellite images with the help of machine learning techniques. Our approach demonstrates the prediction of cluster wealth score and establishes the relationship between wealth score obtained from Demographic and Health Survey (DHS) data and remote sensing satellite images of India by calculating Pearson’s correlation coefficient (r2). The implementation results have been analyzed in four phases. Phase 1 comprises four regression models (RMs): Ridge, RANSAC, Lasso, and k-nearest neighbor for feature extraction from a pre-trained convolutional neural network model using daylight & nightlight images. Here, the Lasso RM outperforms the others and is best suited for predicting the wealth score. Phase 2 categorizes daylight images with DHS data, where the Lasso RM efficiently generates the cluster wealth score. Phase 3 focuses on images of specific regions of Delhi, Tamil Nadu, Maharashtra and Telangana, using the Lasso RM, as it emerged as the best predictor of cluster wealth score in the previous two phases. Phase 4 compares the results attained through our proposed model with existing results
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