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

DSM Building Shape Refinement from Combined Remote Sensing Images based on Wnet-cGANs