Volunteered geographic information contains abundant valuable data, which can be applied to various spatiotemporal geographical analyses. While the useful information may be distributed in different, low‐quality data sources, this issue can be solved by data integration. Generally, the primary task of integration is data matching. Unfortunately, due to the complexity and irregularities of multi‐source data, existing studies have found it difficult to efficiently establish the correspondence between different sources. Therefore, we present a multi‐stage method to match multi‐source data using points of interest. A spatial filter is constructed to obtain candidate sets for geographical entities. The weights of non‐spatial characteristics are examined by a machine learning‐related algorithm with artificially labeled random samples. A case study on Fuzhou reveals that an average of 95% of instances are accurately matched. Thus, our study provides a novel solution for researchers who are engaged in data mining and related work to accurately match multi‐source data via knowledge obtained by the idea and methods of machine learning.

中文翻译：

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使用多元加权函数和梯度下降优化的兴趣点匹配方法

自愿的地理信息包含大量有价值的数据，可以将其应用于各种时空地理分析。尽管有用的信息可能分布在不同的低质量数据源中，但是可以通过数据集成来解决此问题。通常，集成的主要任务是数据匹配。不幸的是，由于多源数据的复杂性和不规则性，现有研究发现很难有效地建立不同源之间的对应关系。因此，我们提出了一种使用兴趣点匹配多源数据的多阶段方法。构造空间滤波器以获得地理实体的候选集。非空间特征的权重通过与机器学习相关的算法与人工标记的随机样本进行检验。以福州为例的研究表明，平均有95％的实例是准确匹配的。因此，我们的研究为从事数据挖掘和相关工作的研究人员提供了一种新颖的解决方案，以通过机器学习的思想和方法获得的知识来准确地匹配多源数据。