  Obtaining reliable data describing local Food Security Metrics (FSM) at a
granularity that is informative to policy-makers requires expensive and
logistically difficult surveys, particularly in the developing world. We train
a CNN on publicly available satellite data describing land cover classification
and use both transfer learning and direct training to build a model for FSM
prediction purely from satellite imagery data. We then propose efficient
tasking algorithms for high resolution satellite assets via transfer learning,
Markovian search algorithms, and Bayesian networks.
