  We present the workflow of a DSM refinement methodology using a Hybrid-cGAN
where the generative part consists of two encoders and a common decoder which
blends the spectral and height information within one network. The inputs to
the Hybrid-cGAN are single-channel photogrammetric DSMs with continuous values
and single-channel pan-chromatic (PAN) half-meter resolution satellite images.
Experimental results demonstrate that the earlier information fusion from data
with different physical meanings helps to propagate fine details and complete
an inaccurate or missing 3D information about building forms. Moreover, it
improves the building boundaries making them more rectilinear.
