  Unsupervised domain adaptation (UDA) is an approach to minimizing domain gap.
Generative methods are common approaches to minimizing the domain gap of aerial
images which improves the performance of the downstream tasks, e.g.,
cross-domain semantic segmentation. For aerial images, the digital surface
model (DSM) is usually available in both the source domain and the target
domain. Depth information in DSM brings external information to generative
models. However, little research utilizes it. In this paper, depth-assisted
ResiDualGAN (DRDG) is proposed where depth supervised loss (DSL), and depth
cycle consistency loss (DCCL) are used to bring depth information into the
generative model. Experimental results show that DRDG reaches state-of-the-art
accuracy between generative methods in cross-domain semantic segmentation
tasks.

Computer Vision and Pattern Recognition