The URBN-LMC model simulates future building morphology attributes (height and footprint) as a rasterized grid at high spatial resolutions (30-m). To our knowledge, such a model capability is not currently widely available. The model relies on high-resolution (30-m to 1km) landcover, population, and land zoning information as inputs to machine learning algorithms to create future building attribute simulations. Land cover projections for urban areas, analogous to National Land Cover Dataset, are taken from a high-resolution modified DEMETER workflow, whereas population grid inputs are simulated products of the SELECT model. The outputs of URBN-LMC are rasterized products of building height and footprint area – these spatially explicit attributes of building morphology will be used by NATURF and AutoBEM to develop highly resolved renderings of future building structures – their exact location and building 3-D structure.