Reliable data on economic livelihoods remain scarce in the developing world, hampering efforts to study these outcomes and to design policies that improve them. Here we demonstrate an accurate, inexpensive, and scalable method for estimating consumption expenditure and asset wealth from high-resolution satellite imagery. Using survey and satellite data from five African countries-Nigeria, Tanzania, Uganda, Malawi, and Rwanda-we show how a convolutional neural network can be trained to identify

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... mage features that can explain up to 75% of the variation in local-level economic outcomes. Our method, which requires only publicly available data, could transform efforts to track and target poverty in developing countries. It also demonstrates how powerful machine learning techniques can be applied in a setting with limited training data, suggesting broad potential application across many scientific domains. W hen an isolated quantum system is perturbed-for instance, owing to a sudden change in the Hamiltonian (a socalled quench)-the ensuing dynamics are determined by an eigenstate distribution that is induced by the quench (1). At any given time, the evolving quantum state will have amplitudes that depend on the eigenstates populated by the quench and the energy eigenvalues of the Hamiltonian. In many cases, however,