  We describe the workflow of a digital surface models (DSMs) refinement
algorithm using a hybrid conditional generative adversarial network (cGAN)
where the generative part consists of two parallel networks merged at the last
stage forming a WNet architecture. The inputs to the so-called WNet-cGAN are
stereo DSMs and panchromatic (PAN) half-meter resolution satellite images.
Fusing these helps to propagate fine detailed information from a spectral image
and complete the missing 3D knowledge from a stereo DSM about building shapes.
Besides, it refines the building outlines and edges making them more
rectangular and sharp.
