Ground-level horizon imagery is scarce across planetary bodies, making representation-centred approaches attractive for down-stream geospatial tasks. We present DESPINA, a geospatial representation system that converts digital elevation models (DEMs) into structured neural embeddings of terrain geometry that condition a diffusion model to produce geometry-preserving, terrain-consistent visual reconstructions for a specified location and view direction.</p> <p>Our pipeline integrates numeric elevation data (DEMs), structural embeddings (inverse-depth and soft edges), and textual priors, unifying heterogeneous geospatial signals into a shared, metric conditioning space. Using a Stable Diffusion model constrained with ControlNet, we can generate geologically consistent yet texturally diverse horizon datasets. Appearance priors are learned from historical surface photography to capture realistic textures and lighting cues, and geometric validation is performed against DEM-derived skylines and depth structure, independent of photographic training data. Through quantitative evaluation and a pilot qualitative study, DESPINA maintains skyline fidelity and geological boundaries while improving structural similarity relative to an image-conditioned baseline. Although our experiments use lunar DEMs and historical surface photography, the method is domain-agnostic and applicable to Earth, Mars, and other planetary DEMs.