  Obtaining reliable data describing local poverty metrics at a granularity
that is informative to policy-makers requires expensive and logistically
difficult surveys, particularly in the developing world. Not surprisingly, the
poverty stricken regions are also the ones which have a high probability of
being a war zone, have poor infrastructure and sometimes have governments that
do not cooperate with internationally funded development efforts. We train a
CNN on free and publicly available daytime satellite images of the African
continent from Landsat 7 to build a model for predicting local economic
livelihoods. Only 5% of the satellite images can be associated with labels
(which are obtained from DHS Surveys) and thus a semi-supervised approach using
a GAN (similar to the approach of Salimans, et al. (2016)), albeit with a more
stable-to-train flavor of GANs called the Wasserstein GAN regularized with
gradient penalty(Gulrajani, et al. (2017)) is used. The method of multitask
learning is employed to regularize the network and also create an end-to-end
model for the prediction of multiple poverty metrics.
