  Global surface water detection in very-high-resolution (VHR) satellite
imagery can directly serve major applications such as refined flood mapping and
water resource assessment. Although achievements have been made in detecting
surface water in small-size satellite images corresponding to local geographic
scales, datasets and methods suitable for mapping and analyzing global surface
water have yet to be explored. To encourage the development of this task and
facilitate the implementation of relevant applications, we propose the
GLH-water dataset that consists of 250 satellite images and manually labeled
surface water annotations that are distributed globally and contain water
bodies exhibiting a wide variety of types (e.g., rivers, lakes, and ponds in
forests, irrigated fields, bare areas, and urban areas). Each image is of the
size 12,800 $\times$ 12,800 pixels at 0.3 meter spatial resolution. To build a
benchmark for GLH-water, we perform extensive experiments employing
representative surface water detection models, popular semantic segmentation
models, and ultra-high resolution segmentation models. Furthermore, we also
design a strong baseline with the novel pyramid consistency loss (PCL) to
initially explore this challenge. Finally, we implement the cross-dataset and
pilot area generalization experiments, and the superior performance illustrates
the strong generalization and practical application of GLH-water. The dataset
is available at https://jack-bo1220.github.io/project/GLH-water.html.
