SatSplat: Geometrically-Accurate Gaussian Splatting for Satellite Imagery

Abstract

High-resolution satellite imagery demands three-dimensional (3D) reconstruction methods that deliver both speed and geometric accuracy. Recent adaptations of 3D Gaussian splatting (3DGS) to satellite imagery demonstrate strong efficiency, but reconstruction quality often degrades under diverse illumination across multi-date, high-altitude acquisitions (with small intersection angles), limiting applicability to remote sensing and vision tasks. We present SatSplat, the first framework to adapt 2D Gaussian splatting (2DGS) to satellite photogrammetry, with online camera adjustment. We approximated satellite cameras with an affine model and learned a minimal delta parameterization for in-splat camera refinement from dense observations. The formulation was implemented with a 2DGS scene representation. To handle time-varying shadows and illumination changes, we integrated geometric shadow mapping and per-camera color correction during training. Across the evaluated DFC2019 and IARPA2016 benchmark sites, SatSplat achieved strong geometric accuracy while significantly outperforming prior 3DGS-based baselines. On our processed DFC2019 benchmark, SatSplat reduced mean absolute error by 11.93% and peak video memory by 31% relative to the previous state of the art. Our approach enabled large-scale digital surface modeling with practical computational efficiency. The project page is available at https://gdaosu.github.io/satsplat.