Abstract: To improve the segmentation performance and solve the problem of poor generalization of the model in different data domains, we propose a method based on depth information for semantic segmentation in the context of unsupervised domain adaptation. It includes a Depth-aware Adaptation Framework(DAF) and a Intra-domain Adaptation(IDA) strategy. Firstly, DAF is proposed to adapt domains by capitalizing on the inherent correlations of semantic and depth information. Then a novel light-weight depth estimation network is designed provide additional depth information, and we fuse semantic and depth information by cross-task interaction, then align the distribution in depth-aware space between source and target domains. Finally, IDA strategy is proposed to bridge the distribution gap inside the target domain. To this end, a depth-aware ranking strategy is presented to separate target domain into sub-source domain and sub-target domain, and then we perform the alignment between sub-source domain and sub-target domain. Experiments on SYNTHIA-2-Cityscapes and SYNTHIA-2-Mapillary cross-domain tasks show that our method achieves significant improvement(46.7% mIoU and 73.3% mIoU, respectively) compared with the similar methods.