Agricultural parcels serve as basic units for conducting agricultural practices and applications, which is vital for land ownership registration, food security assessment, soil erosion monitoring, etc. However, existing agriculture parcel extraction studies only focus on mid-resolution mapping or regular plain farmlands while lacking representation of complex terraced terrains due to the demands of precision agriculture.In this paper, we introduce a more fine-grained terraced parcel dataset named GTPBD (Global Terraced Parcel and Boundary Dataset), which is the first fine-grained dataset covering major worldwide terraced regions with more than 200,000 complex terraced parcels with manual annotation. GTPBD comprises 47,537 high-resolution images with three-level labels, including pixel-level boundary labels, mask labels, and parcel labels. It covers seven major geographic zones in China and transcontinental climatic regions around the world.Compared to the existing datasets, the GTPBD dataset brings considerable challenges due to the: (1) terrain diversity; (2) complex and irregular parcel objects; and (3) multiple domain styles. Our proposed GTPBD dataset is suitable for four different tasks, including semantic segmentation, edge detection, terraced parcel extraction, and unsupervised domain adaptation (UDA) tasks.Accordingly, we benchmark the GTPBD dataset on eight semantic segmentation methods, four edge extraction methods, three parcel extraction methods, and five UDA methods, along with a multi-dimensional evaluation framework integrating pixel-level and object-level metrics. GTPBD fills a critical gap in terraced remote sensing research, providing a basic infrastructure for fine-grained agricultural terrain analysis and cross-scenario knowledge transfer.

翻译：农业地块是开展农业实践与应用的基本单元，对于土地所有权登记、粮食安全评估、土壤侵蚀监测等至关重要。然而，现有的农业地块提取研究仅关注中分辨率制图或规则的平原农田，由于精细农业的需求，缺乏对复杂梯田地形的表征。本文介绍了一个更细粒度的梯田地块数据集，命名为GTPBD（全球梯田地块与边界数据集），这是首个覆盖全球主要梯田区域、包含超过20万个复杂梯田地块并经过人工标注的细粒度数据集。GTPBD包含47,537张高分辨率图像及三级标签，包括像素级边界标签、掩码标签和地块标签。它涵盖了中国七大地理区域以及世界范围内的跨大陆气候区。与现有数据集相比，GTPBD数据集因以下特点带来了显著挑战：(1) 地形多样性；(2) 复杂且不规则的地块对象；(3) 多域风格。我们提出的GTPBD数据集适用于四种不同任务，包括语义分割、边缘检测、梯田地块提取和无监督域适应（UDA）任务。相应地，我们在GTPBD数据集上对八种语义分割方法、四种边缘提取方法、三种地块提取方法和五种UDA方法进行了基准测试，并辅以一个整合像素级和对象级指标的多维评估框架。GTPBD填补了梯田遥感研究中的一个关键空白，为细粒度农业地形分析和跨场景知识迁移提供了基础支撑。