Researchers propose enhancing photogrammetric Digital Surface Models (DSMs) derived from satellite stereo imagery by using pretrained diffusion models with multimodal conditioning, addressing noise, outliers, and voids that contaminate large-scale 3D maps. The approach contrasts with aerial LiDAR, which provides high-accuracy elevation measurements at a substantially higher cost.
Large-scale Digital Surface Models (DSMs) can be produced cost-effectively from satellite images via stereo-photogrammetry. However, the resulting 3D maps are often contaminated by noise, outliers, and voids. On the other hand, aerial LiDAR provides high-accuracy elevation measurements at a substant
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