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In many developing nations, a lack of poverty data prevents criticalhumanitarian organizations from responding to large-scale crises. Currently,socioeconomic surveys are the only method implemented on a large scale fororganizations and researchers to measure and track poverty. However, theinability to collect survey data efficiently and inexpensively leads tosignificant temporal gaps in poverty data; these gaps severely limit theability of organizational entities to address poverty at its root cause. Wepropose a transfer learning model based on surface temperature change andremote sensing data to extract features useful for predicting poverty rates.Machine learning, supported by data sources of poverty indicators, has thepotential to estimate poverty rates accurately and within strict timeconstraints. Higher temperatures, as a result of climate change, have causednumerous agricultural obstacles, socioeconomic issues, and environmentaldisruptions, trapping families in developing countries in cycles of poverty. Tofind patterns of poverty relating to temperature that have the highestinfluence on spatial poverty rates, we use remote sensing data. The two-steptransfer model predicts the temperature delta from high resolution satelliteimagery and then extracts image features useful for predicting poverty. Theresulting model achieved 80% accuracy on temperature prediction. This methodtakes advantage of abundant satellite and temperature data to measure povertyin a manner comparable to the existing survey methods and exceeds similarmodels of poverty prediction.

Novel Machine Learning Approach for Predicting Poverty using Temperature and Remote Sensing Data in Ethiopia