Hall Joanne V, Argueta Fernanda, Giglio Louis
Department of Geographical Sciences, University of Maryland, 2181 Samuel J. LeFrak Hall, 7251 Preinkert Drive, College Park, MD 20742, United States of America.
Data Brief. 2024 Jul 14;55:110739. doi: 10.1016/j.dib.2024.110739. eCollection 2024 Aug.
This dataset consists of 190,832 manually-digitized cropland field boundaries, with associated attributes, within Brazil, Ukraine, United States of America, Canada, and Russia. Specifically, 22 regions of various sizes (74km - 38,000km) spanning 5 countries were digitized over a range of predominant crop types over different time periods. These field boundaries were drawn over 20 m Sentinel-2 imagery. This field boundary dataset is a byproduct of a larger effort to map cropland burned area (Global Cropland Area Burned: GloCAB product [1]), however, it has several benefits beyond its original intent, including as a training dataset for machine-learning field size analyses, or a dataset to derive cropland field characteristics across different predominant crop types and geographies.
该数据集由巴西、乌克兰、美利坚合众国、加拿大和俄罗斯境内190,832个手动数字化的农田边界及其相关属性组成。具体而言,跨越5个国家的22个不同大小(74公里 - 38,000公里)的区域在不同时间段内针对一系列主要作物类型进行了数字化处理。这些农田边界是在20米分辨率的哨兵 - 2影像上绘制的。这个农田边界数据集是绘制农田烧毁面积的一项更大规模工作(全球农田烧毁面积:GloCAB产品[1])的副产品,然而,它除了最初的用途外还有几个好处,包括作为机器学习田间规模分析的训练数据集,或用于推导不同主要作物类型和地理区域的农田特征的数据集。