  Combining satellite imagery with machine learning (SIML) has the potential to
address global challenges by remotely estimating socioeconomic and
environmental conditions in data-poor regions, yet the resource requirements of
SIML limit its accessibility and use. We show that a single encoding of
satellite imagery can generalize across diverse prediction tasks (e.g. forest
cover, house price, road length). Our method achieves accuracy competitive with
deep neural networks at orders of magnitude lower computational cost, scales
globally, delivers label super-resolution predictions, and facilitates
characterizations of uncertainty. Since image encodings are shared across
tasks, they can be centrally computed and distributed to unlimited researchers,
who need only fit a linear regression to their own ground truth data in order
to achieve state-of-the-art SIML performance.
