  In many developing nations, a lack of poverty data prevents critical
humanitarian organizations from responding to large-scale crises. Currently,
socioeconomic surveys are the only method implemented on a large scale for
organizations and researchers to measure and track poverty. However, the
inability to collect survey data efficiently and inexpensively leads to
significant temporal gaps in poverty data; these gaps severely limit the
ability of organizational entities to address poverty at its root cause. We
propose a transfer learning model based on surface temperature change and
remote sensing data to extract features useful for predicting poverty rates.
Machine learning, supported by data sources of poverty indicators, has the
potential to estimate poverty rates accurately and within strict time
constraints. Higher temperatures, as a result of climate change, have caused
numerous agricultural obstacles, socioeconomic issues, and environmental
disruptions, trapping families in developing countries in cycles of poverty. To
find patterns of poverty relating to temperature that have the highest
influence on spatial poverty rates, we use remote sensing data. The two-step
transfer model predicts the temperature delta from high resolution satellite
imagery and then extracts image features useful for predicting poverty. The
resulting model achieved 80% accuracy on temperature prediction. This method
takes advantage of abundant satellite and temperature data to measure poverty
in a manner comparable to the existing survey methods and exceeds similar
models of poverty prediction.
