Land-use change alters ecosystems and the services they provide, but calculating these effects requires high-resolution land-use maps. Ecosystem service models rely on satellite-derived maps for present and historical conditions, using spatial resolutions of 10 to 300 meters. Scenarios of future land-use dynamics, however, are typically provided at resolutions of 0.25 arc-degrees (~30 km at the equator) or coarser, far too coarse for ecosystem-service models. Here, we describe and evaluate the Spatial Economic Allocation Landscape Simulator (SEALS) model, a global, computationally tractable model that downscales coarse land-use projections into high-resolution maps suitable for ecosystem service analysis. SEALS estimates 3.12 million parameters applied to spatial layers for land constraints, transition suitability and multi-scale adjacency convolutions, identified separately for each 1-degree tile globally. The parameters are trained on observed land-use change from 2000 to 2015 and validated on withheld data from 2020. SEALS can generate global 10 arc-second land-use maps in minutes on a standard laptop. We show that SEALS places change 1.9 times more accurately than a random allocation of the same change and that the advantage persists when near-misses are credited. Passing the downscaled maps to the InVEST habitat-quality model reproduces habitat quality calculated from observed land cover more closely than coarse accounting in nine of ten deforestation regions, showing that resolving where change falls improves a downstream ecosystem-service estimate. As a demonstration we downscale eight Shared Socioeconomic Pathway scenarios to 2100 and release the resulting global 10 arc-second land-use maps.