Existing predictive mapping methods usually require a large number of field samples with good representativeness as input to build reliable predictive models. In mapping practice, however, we often face situations when only small sample data are available. In this article, we present a semi‐supervised machine learning approach for predictive mapping in which the natural aggregation (clustering) patterns of environmental covariate data are used to supplement limited samples in prediction. This approach was applied to two soil mapping case studies. Compared with field sample only approaches (decision trees, logistic regression, and support vector machines), maps using the proposed approach can better capture the spatial variation of soil types and achieve higher accuracy with limited samples. A cross validation shows further that the proposed approach is less sensitive to the specific field sample set used and thus more robust when field sample data are small.

中文翻译：

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使用半监督机器学习对小范围样本数据进行预测映射

现有的预测映射方法通常需要大量具有良好代表性的现场样本作为输入，以建立可靠的预测模型。但是，在制图实践中，我们经常会遇到只有少量样本数据可用的情况。在本文中，我们提出了一种用于预测映射的半监督机器学习方法，其中，环境协变量数据的自然聚集（聚类）模式用于补充预测中的有限样本。该方法已应用于两个土壤测绘案例研究。与仅使用现场样本的方法（决策树，逻辑回归和支持向量机）相比，使用该方法的地图可以更好地捕获土壤类型的空间变化，并在有限的样本下获得更高的精度。