Ali Jamali, Swalpa Kumar Roy, Jonathan Li, and Pedram Ghamisi
In the domain of remote sensing image interpretation, road extraction from high-resolution aerial imagery has already been a hot research topic. Although deep CNNs have presented excellent results for semantic segmentation, the efficiency and capabilities of vision transformers are yet to be fully researched. As such, for accurate road extraction, a deep semantic segmentation neural network that utilizes the abilities of residual learning, HetConvs, UNet, and vision transformers, which is called ResUNetFormer, is proposed in this letter. The developed ResUNetFormer is evaluated on various cutting-edge deep learning-based road extraction techniques on the public Massachusetts road dataset. Statistical and visual results demonstrate the superiority of the ResUNetFormer over the state-of-the-art CNNs and vision transformers for segmentation. The code will be available on GitHub.