Unconditional cash transfers to the extreme poor via mobile telephony represent a radical, new approach to giving. GiveDirectly is a non-governmental organization (NGO) at the vanguard of delivering this proven and effective approach to reducing poverty. In this work, we streamline an important step in the operations of the NGO by developing and deploying a data-driven system for locating villages with extreme poverty in Kenya and Uganda. Using the type of roof of a home, thatched or metal, as a proxy for poverty, we develop a new remote sensing approach for selecting extremely poor villages to target for cash transfers. We develop an analytics algorithm that estimates housing quality and density in patches of publicly-available satellite imagery by learning a predictive model with sieves of template matching results combined with color histograms as features. We develop and deploy a crowdsourcing interface to obtain labeled training data. We deploy the predictive model to construct a fine-scale heat map of poverty and integrate this discovered knowledge into the processes of GiveDirectly's operations. Aggregating estimates at the village level, we produce a ranked list from which top villages are included in GiveDirectly's planned distribution of cash transfers. The automated approach increases village selection efficiency significantly.