  Climate change is expected to reshuffle the settlement landscape: forcing
people in affected areas to migrate, to change their lifeways, and continuing
to affect demographic change throughout the world. Changes to the geographic
distribution of population will have dramatic impacts on land use and land
cover and thus constitute one of the major challenges of planning for climate
change scenarios. In this paper, we explore a generative model framework for
generating satellite imagery conditional on gridded population distributions.
We make additions to the existing ALAE architecture, creating a spatially
conditional version: SCALAE. This method allows us to explicitly disentangle
population from the model's latent space and thus input custom population
forecasts into the generated imagery. We postulate that such imagery could then
be directly used for land cover and land use change estimation using existing
frameworks, as well as for realistic visualisation of expected local change. We
evaluate the model by comparing pixel and semantic reconstructions, as well as
calculate the standard FID metric. The results suggest the model captures
population distributions accurately and delivers a controllable method to
generate realistic satellite imagery.
