Waterline Extraction for Artificial Coast With Vision Transformers - Yang, Le; Wang, Xing; Zhai, Jingsheng (Frontiers in Environmental Science, 2022)
Accurate acquisition for the positions of the waterlines plays a critical role in coastline extraction. However, waterline extraction from high-resolution images is a very challenging task, because it is easily influenced by the complex background. To fulfill the task, two type of vision Transformers, segmentation Transformers (SETR) and semantic segmentation Transformers (SegFormer) are introduced as an early exploration of the potential of Transformers for waterline extraction. To estimate the effects of two methods, we collect the high-resolution images from the web map services, and the annotations are created manually for training and test. Through extensive experiments, Transformer-based approaches achieved state-of-the-art performances for waterline extraction in the artificial coast.