Enhancing Photogrammetric Digital Surface Models with Pretrained Diffusion Models and Multimodal Conditioning

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