  Determining the poverty levels of various regions throughout the world is
crucial in identifying interventions for poverty reduction initiatives and
directing resources fairly. However, reliable data on global economic
livelihoods is hard to come by, especially for areas in the developing world,
hampering efforts to both deploy services and monitor/evaluate progress. This
is largely due to the fact that this data is obtained from traditional
door-to-door surveys, which are time consuming and expensive. Overhead
satellite imagery contain characteristics that make it possible to estimate the
region's poverty level. In this work, I develop deep learning computer vision
methods that can predict a region's poverty level from an overhead satellite
image. I experiment with both daytime and nighttime imagery. Furthermore,
because data limitations are often the barrier to entry in poverty prediction
from satellite imagery, I explore the impact that data quantity and data
augmentation have on the representational power and overall accuracy of the
networks. Lastly, to evaluate the robustness of the networks, I evaluate them
on data from continents that were absent in the development set.
