Borg, OliverGain, JamesGuérin, ÉricPeytavie, AdrienCani, Marie-PauleGalin, EricCordonnier, GuillaumeMasia, BelenThies, Justus2026-04-212026-04-2120261467-8659https://diglib.eg.org/handle/10.1111/cgf70390https://doi.org/10.1111/cgf.70390To support the design and subsequent generation of terrestrial planets for use in the creative media, we propose a solution that employs a generative model trained on satellite data from planetary bodies with a defined solid surface, such as the Earth and Mars. A user sketches coarse elevation, landcover, temperature, and precipitation directly onto a globe. Our model then infers high-resolution heightmap and surface appearance layers at planetary scales, with sufficient detail to enable animated flyovers within the exosphere at a distance of a few thousand kilometers from the planet surface. We address the issue of distortion in the mapping from atlas to globe using a quadsphere representation, and the consistency of large-scale geomorphological features by extracting a global river network from the sketch inputs and providing this as conditioning to the diffusion. As our results demonstrate, our generative model provides a balance between: authoring control through a multi-layer painting interface with a satellite image pre-visualization; computation times proportional to the surface area being generated; landscape diversity, displaying, without repetition artefacts, the full range of elevation and landcover features drawn from multiple source planets, and geomorphological plausibility through the provision of a consistent uninterrupted exorheic global river network, where the input sketches allow.CC-BY-4.0CCS Concepts: Computing methodologies → Machine learningComputer graphicsShape modeling10.1111/cgf.7039015 pages