Improving Road Extraction for Autonomous Driving Using Swin Transformer Unet

Abstract

One of the most important tasks in the advanced transportation systems is road extraction. Unlike single geospatial objects extraction, extracting road region from high-resolution remote sensing imagery is challenging due to complicated background and rural road networks that have heterogeneous forms with low interclass and high intraclass differences. In the past few years, convolutional neural networks (CNNs) have achieved milestones in road extraction. In particular, the networks based on U -shaped architecture and skip-connections have been widely used in a variety of road extraction tasks. However, although CNN has achieved excellent performance, global and long-range semantic information interaction cannot be learned well due to the locality of convolution operation. In this paper, we propose Swin Transformer Unet which combined U-shaped architecture with hierarchical Swin Transformer with shifted windows. Moreover, we improve the loss function to make it more suitable for our road extraction task. The techniques of data augmentation and data preprocessing are used in order to get better performance. The Massachusetts roads dataset is chosen as the dataset to carry out the experiment of road extraction, and the result shows that this model outperforms other U-shaped networks of road extraction from remote sensing images.