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.

翻译：甚高分辨率卫星影像中的全球地表水体检测可直接服务于精细化洪水制图、水资源评估等重大应用。尽管针对局部地理尺度的小幅面卫星影像水体检测已取得进展，但目前仍缺乏适用于全球地表水制图与分析的数据集与方法。为推动该任务发展并促进相关应用落地，我们提出GLH-water数据集，包含250幅全球分布的卫星影像及其人工标注的地表水体标签，涵盖多种水体类型（如森林、灌溉农田、裸地及城市区域中的河流、湖泊和池塘）。每幅影像空间分辨率为0.3米，尺寸为12800×12800像素。为构建GLH-water基准，我们开展了大量实验，采用代表性地表水体检测模型、主流语义分割模型及超高分剖分割模型。此外，我们设计了基于新型金字塔一致性损失（PCL）的强基线方法，初步探索该挑战任务。最后，通过跨数据集与先导区泛化实验，优越性能验证了GLH-water的强泛化能力与实用价值。数据集访问地址：https://jack-bo1220.github.io/project/GLH-water.html