Abstract
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 this http URL 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 this http URL 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) this http URL, 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.
Abstract (translated)
农业地块作为进行农业生产活动和应用的基本单元，对于土地所有权注册、粮食安全评估、土壤侵蚀监测等方面至关重要。然而，现有的农业地块提取研究主要集中在中分辨率地图或常规平原农田上，并且由于技术要求的限制，缺乏对复杂梯田地形的表现。 在本文中，我们引入了一个更精细划分的梯田数据集——GTPBD（全球梯田边界和地块数据集）。该数据集是首个覆盖世界各大梯田区域、包含超过20万份复杂梯田地块的手工标注数据集。GTPBD包括47,537张高分辨率图像，带有三级标签，包括像素级边界标签、掩码标签以及地块标签。它涵盖了中国七大主要地理区域及跨大陆的气候区。 与现有数据集相比，GTPBD数据集带来了三个方面的重大挑战：（1）地形多样性；（2）复杂且不规则的地块对象；（3）多种领域风格。我们提出的GTPBD数据集适合四种不同的任务，包括语义分割、边缘检测、梯田地块提取以及无监督域适应（UDA）。此外，我们在八个语义分割方法、四个边缘抽取方法、三个地块提取方法及五个UDA方法上对GTPBD进行了基准测试，并引入了一个结合像素级和对象级指标的多维度评估框架。 GTPBD填补了梯田遥感研究中的一个重要空白，为精细农业地形分析以及跨场景知识迁移提供了一项基础性基础设施。
URL
https://arxiv.org/abs/2507.14697
