challenges by remotely estimating socioeconomic and environmental conditions in data-poorregions, yet the resource requirements of SIML limit its accessibility and use. We show that asingle encoding of satellite imagery can generalize across diverse prediction tasks (e.g., forestcover, house price, road length). Our method achieves accuracy competitive with deep neuralnetworks at orders of magnitude lower computational cost, scales globally, delivers labelsuper-resolution predictions, and facilitates characterizations of uncertainty. Since imageencodings are shared across tasks, they can be centrally computed and distributed tounlimited researchers, who need only ﬁt a linear regression to their own ground truth data in