Greenspace has positive influences on urban environment and human health, and thus it is desirable to acquire data for (urban) greenspace mapping. Nowadays, global and regional open land-use/land-cover datasets have become essential sources for greenspace mapping, but few studies have quantitatively compared them. To fill this gap, this study carries out a quantitative comparison of six global and regional open datasets (CGLS-LC100, CLC, GLC30, UA, FROM-GLC10 and OSM) for greenspace mapping. First of all, the most appropriate land-use/land-cover classes selected as greenspace are analyzed for each open dataset; then, different open datasets are evaluated and compared in terms of five measures (accuracy, precision, recall, F1-score and green coverage rate). Five urban areas in UK are chosen as study areas. Two categories of reference datasets are used for evaluation, including an Ordnance Survey (OS) greenspace dataset in UK and a number of sampling points classified by referring to Google Earth. Results show that: the OSM dataset performs the best, while comparing with the OS dataset (characterized by a narrowly interpreted greenspace); and the FROM-GLC10 dataset performs the best, while comparing with the sampling points (characterized by a broadly interpreted greenspace). Moreover, by using these two open datasets, most quantitative results are close to or higher than 80 %, in terms of the accuracy, precision, recall and F1-score; in most cases there also is the smallest difference between using these two open datasets and corresponding reference datasets, in terms of the green coverage rate. These findings have benefits for researchers and planners to choose an appropriate open dataset for greenspace mapping.

绿地对城市环境和人类健康具有积极影响，因此，需要获取（城市）绿地地图数据。如今，全球和区域开放的土地利用/土地覆盖数据集已成为进行绿地制图的重要资源，但很少有研究对其进行定量比较。为了填补这一空白，本研究对六个全球和区域开放数据集（CGLS-LC100，CLC，GLC30，UA，FROM-GLC10和OSM）进行了定量比较，以进行绿地制图。首先，为每个开放数据集分析被选为绿地的最合适的土地利用/土地覆盖类别；然后，根据五个度量（准确性，精度，召回率，F1得分）对不同的开放数据集进行评估和比较和绿色覆盖率）。选择了英国的​​五个城市地区作为研究区域。两类参考数据集用于评估，包括英国的军械测量（OS）绿地数据集和一些通过引用Google Earth分类的采样点。结果表明：与OS数据集（以狭义解释的绿色空间为特征）相比，OSM数据集表现最佳。和FROM-GLC10数据集的效果最佳，同时与采样点进行比较（以广泛解释的绿色空间为特征）。此外，通过使用这两个开放数据集，就准确性，精度，召回率和F1得分而言，大多数定量结果都接近或高于80％。; 在大多数情况下，就绿色覆盖率而言，使用这两个开放数据集和相应的参考数据集之间的差异最小。这些发现对于研究人员和计划人员选择合适的开放数据集进行绿地映射很有帮助。