  Obtaining detailed and reliable data about local economic livelihoods in
developing countries is expensive, and data are consequently scarce. Previous
work has shown that it is possible to measure local-level economic livelihoods
using high-resolution satellite imagery. However, such imagery is relatively
expensive to acquire, often not updated frequently, and is mainly available for
recent years. We train CNN models on free and publicly available multispectral
daytime satellite images of the African continent from the Landsat 7 satellite,
which has collected imagery with global coverage for almost two decades. We
show that despite these images' lower resolution, we can achieve accuracies
that exceed previous benchmarks.
