Sand dune patterns (SDPs) are spatial aggregations of dunes and interdunes, exhibiting distinct morphologies and spatial structures. Recognizing global SDPs is crucial for understanding the development processes, contributing factors, and self-organization characteristics of aeolian systems. However, the diversity, complexity, and multiscale nature of global SDPs poses significant technical challenges in the classification scheme, sample collection, feature representation, and classification method. This study addresses these challenges by developing a novel global SDP classification approach based on an advanced deep-learning network. Firstly, we established a globally applicable SDP classification scheme that accommodates the diversity nature of SDPs. Secondly, we developed an SDP semantic segmentation sample dataset, which encompassed a wide array of SDP representations. Thirdly, we deployed the SegFormer network to automatically capture detailed dune structures and developed a weighted voting strategy to ensure scale adaptability. Experiments utilizing Landsat-8 imagery yielded a commendable overall accuracy (OA) of 85.43 %. Notably, most SDP types exhibited high classification accuracies, such as star dunes (97.43 %) and simple linear dunes (87.17 %). The weighted voting strategy prioritized the predictions of each type, resulting in a 1.41 %∼7.91 % improvement in OA compared to the single-scale classification and average voting methods. This innovative approach facilitated the generation of a high-quality, fine-grained, and global-scale SDP map at 30 m resolution (GSDP30), which not only directly provides the spatial distribution of global SDPs but also serves as valuable support for understanding aeolian processes. This study represents the first instance of producing such a comprehensive and globally applicable SDP map at this fine resolution.

中文翻译：

---

沙丘模式的全球视角：使用 Landsat 影像和深度学习策略进行比例适应性分类


沙丘模式（SDPs）是沙丘和沙丘间的空间聚集体，表现出不同的形态和空间结构。识别全局 SDP 对于理解风沙系统的发展过程、影响因素和自组织特征至关重要。然而，全球 SDP 的多样性、复杂性和多尺度性质在分类方案、样本采集、特征表示和分类方法方面带来了重大的技术挑战。本研究通过开发一种基于高级深度学习网络的新型全局 SDP 分类方法来应对这些挑战。首先，我们建立了一个全球适用的 SDP 分类方案，以适应 SDP 的多样性性质。其次，我们开发了一个 SDP 语义分割样本数据集，其中包含广泛的 SDP 表示。第三，我们部署了 SegFormer 网络来自动捕获详细的沙丘结构，并开发了加权投票策略来确保规模适应性。利用 Landsat-8 影像的实验产生了 85.43% 的值得称赞的总体精度 （OA）。值得注意的是，大多数 SDP 类型表现出较高的分类精度，例如星形沙丘 （97.43 %） 和简单线性沙丘 （87.17 %）。加权投票策略优先考虑每种类型的预测，与单一尺度分类和平均投票方法相比，OA 提高了 1.41 %∼7.91 %。这种创新方法有助于生成 30 m 分辨率 （GSDP30） 的高质量、细粒度和全球尺度的 SDP 图，这不仅直接提供了全球 SDP 的空间分布，而且为理解风积过程提供了有价值的支持。 这项研究代表了在如此精细的分辨率下制作如此全面且全球适用的 SDP 地图的第一个实例。