The Degree Confluence Project (DCP) is a volunteer-based validation dataset that comprises useful information for global land cover map validation. However, there is a problem with using DCP points as validation data for the accuracy assessment of land cover maps. While resolutions of typical global land cover maps are several hundred meters to several kilometers, DCP points can only guarantee an area of several tens of meters that can be confirmed by ground photographs. So, the objective of this study is to create a land cover map validation dataset with added spatial uniformity information using satellite images and DCP points. For this, we devised a new method to semiautomatically guarantee the spatial uniformity of DCP validation data points at any resolution. This method can judge the validation data with guaranteed uniformity with an accuracy of 0.954. Furthermore, we conducted the accuracy assessment for the existing global land cover maps by the DCP validation data with guaranteed spatial uniformity and found that the trends differed by class and region.

中文翻译：

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使用空间均匀性验证数据集进行全球土地覆盖评估

度汇合项目​​ (DCP) 是一个基于志愿者的验证数据集，其中包含用于全球土地覆盖地图验证的有用信息。然而，使用 DCP 点作为土地覆盖图准确性评估的验证数据存在问题。典型的全球土地覆盖图的分辨率为几百米到几公里，而DCP点只能保证地面照片可以确认的几十米面积。因此，本研究的目的是创建一个土地覆盖图验证数据集，使用卫星图像和 DCP 点添加空间均匀性信息。为此，我们设计了一种新方法来半自动地保证任何分辨率下 DCP 验证数据点的空间均匀性。该方法可以以0.954的准确度判断验证数据，保证一致性。此外，我们通过保证空间均匀性的 DCP 验证数据对现有的全球土地覆盖图进行了准确性评估，发现趋势因类别和区域而异。