# Depth-Assisted ResiDualGAN for Cross-Domain Aerial Images Semantic Segmentation

Source: [Depth-Assisted ResiDualGAN for Cross-Domain Aerial Images Semantic Segmentation](<https://www.sota2.com/research/paper/68fa6d4b1e209131cea38d9c>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:30:35.914Z

Authors: Yang Zhao, Peng Guo, Han Gao, Xiuwan Chen

Published: 2022-08-21T06:58:51.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2208.09823>)

[PDF](<https://arxiv.org/pdf/2208.09823.pdf>)