Neighborhood Attention Makes the Encoder of ResUNet Stronger for
  Accurate Road Extraction
Ali Jamali Swalpa Kumar Roy Jonathan Li 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 \texttt{ResUNetFormer}, is
proposed in this letter. The developed \texttt{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 \texttt{ResUNetFormer} over the state-of-the-art CNNs
and vision transformers for segmentation. The code will be made available
publicly at \url{https://github.com/aj1365/ResUNetFormer}.