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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 types 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 the 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.

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The road network is of great importance to the modern traffic system, which changes frequently as its high speed of development. Simultaneously, a growing number of satellites provide a considerable number of remote sensing images that can be used for road extraction. However, most existing methods cannot get accurate results in the high-resolution remote sensing, since the complex background like the shadows and occlusions. Consequently, we proposed a method utilizing the volunteered geographic information data to extract the road from satellite images. At first, from Mapbox platform, the Volunteered Geographic Information data, OpenStreetMap vector tiles are collected to guide the downloading of corresponding road satellite images. Then a level set method is used to extract the road areas from the satellite images supported by the road vector. After that, a superpixel method is applied to improve the road areas by fusing the superpixels with the previous results. Experiments are performed on the satellite images from Mapbox, Google, and Yahoo in the Tianjin Port district on accuracy and efficiency. Experimental results demonstrate that our approach is more accurate and of higher performance than the other two stateof-the-art methods.