  Accurately segmenting roads is challenging due to substantial intra-class
variations, indistinct inter-class distinctions, and occlusions caused by
shadows, trees, and buildings. To address these challenges, attention to
important texture details and perception of global geometric contextual
information are essential. Recent research has shown that CNN-Transformer
hybrid structures outperform using CNN or Transformer alone. While CNN excels
at extracting local detail features, the Transformer naturally perceives global
contextual information. In this paper, we propose a dual-branch network block
named ConSwin that combines ResNet and SwinTransformers for road extraction
tasks. This ConSwin block harnesses the strengths of both approaches to better
extract detailed and global features. Based on ConSwin, we construct an
hourglass-shaped road extraction network and introduce two novel connection
structures to better transmit texture and structural detail information to the
decoder. Our proposed method outperforms state-of-the-art methods on both the
Massachusetts and CHN6-CUG datasets in terms of overall accuracy, IOU, and F1
indicators. Additional experiments validate the effectiveness of our proposed
module, while visualization results demonstrate its ability to obtain better
road representations.
