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:在甚高分辨率(VHR)卫星图像中,全球地表水探测可直接服务于重大应用,如改进洪水测绘和水资源评估。虽然在根据当地地理规模在小型卫星图像中探测地表水方面取得了与当地地理规模相对应的成就,但是尚未探索适合测绘和分析全球地表水的数据集和方法。为了鼓励制定这项任务并促进相关应用的实施,我们提议GLH-水数据集,其中包括全球分布的250个卫星图像和人工贴标签的地表水说明,其中含有各种类型的水体(例如,河流、湖泊、森林、灌溉田、裸露地区和城市地区的池塘),每个图像的大小为12 800美元/时间,0.3米空间分辨率为12 800美元的像素。为建立GLH-水的基准,我们利用具有代表性的地表水检测模型、广通性分解模型和超高分辨率分辨率分解模型进行广泛的实验。此外,我们还设计了一个强有力的基准,以新的金字塔一致性损失(PCL)、森林、灌溉田、裸露地区和城市地区为最初探索这一挑战的金字塔/GLML实验。最后,我们用GLS-CS-GRG-CS-CS-G-S-G-G-S-G-G-G-S-S-G-G-S-G-S-G-G-S-S-G-G-S-S-S-S-S-S-S-S-S-S-S-S-S-S-G-G-G-G-G-G-S-S-S-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G-G</s>