With recent advances in high-resolution satellite imagery and machine vision algorithms, fine-grain geospatial data on population are now widely available: kilometer-bykilometer, worldwide. In this paper, we showcase how researchers and policymakers in developing countries can leverage these novel data to precisely identify “education deserts” - localized areas where families lack physical access to education - at unprecedented scale, detail, and costeffectiveness. We demonstrate how these analyses could valuably inform educational access initiatives like school construction and transportation investments, and outline a variety of analytic extensions to gain deeper insight into the state of school access across a given country. We conduct a proof-of-concept analysis in the context of Guatemala, which has historically struggled with educational access, as a demonstration of the utility, viability, and flexibility of our proposed approach. We find that the vast majority of Guatemalan population lives within 3 km of a public primary school, indicating a generally low incidence of distance as a barrier to education in that context. However, we still identify concentrated pockets of population for whom the distance to school remains prohibitive, revealing important geographic variation within the strong countrywide average. Finally, we show how even a small number of optimally-placed schools in these areas, using a simple algorithm we develop, could substantially reduce the incidence of “education deserts” in this context. We make our entire codebase available to the public – fully free, opensource, heavily documented, and designed for broad use – allowing analysts across contexts to easily replicate our proposed analyses for other countries, educational levels, and public goods more generally.