  Digital Surface Models (DSM) offer a wealth of height information for
understanding the Earth's surface as well as monitoring the existence or change
in natural and man-made structures. Classical height estimation requires
multi-view geospatial imagery or LiDAR point clouds which can be expensive to
acquire. Single-view height estimation using neural network based models shows
promise however it can struggle with reconstructing high resolution features.
The latest advancements in diffusion models for high resolution image synthesis
and editing have yet to be utilized for remote sensing imagery, particularly
height estimation. Our approach involves training a generative diffusion model
to learn the joint distribution of optical and DSM images across both domains
as a Markov chain. This is accomplished by minimizing a denoising score
matching objective while being conditioned on the source image to generate
realistic high resolution 3D surfaces. In this paper we experiment with
conditional denoising diffusion probabilistic models (DDPM) for height
estimation from a single remotely sensed image and show promising results on
the Vaihingen benchmark dataset.
