The growing use of crowdsourced geographic information (CGI) has prompted the employment of several methods for assessing information quality, which are aimed at addressing concerns on the lack of quality of the information provided by non‐experts. In this work, we propose a taxonomy of methods for assessing the quality of CGI when no reference data are available, which is likely to be the most common situation in practice. Our taxonomy includes 11 quality assessment methods that were identified by means of a systematic literature review. These methods are described in detail, including their main characteristics and limitations. This taxonomy not only provides a systematic and comprehensive account of the existing set of methods for CGI quality assessment, but also enables researchers working on the quality of CGI in various sources (e.g., social media, crowd sensing, collaborative mapping) to learn from each other, thus opening up avenues for future work that combines and extends existing methods into new application areas and domains.

中文翻译：

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用于自愿和众包的地理信息的质量评估方法的分类法。

众包地理信息（CGI）的使用日益广泛，促使人们采用了几种评估信息质量的方法，其目的是解决对非专家提供的信息质量缺乏的担忧。在这项工作中，我们提出了一种分类法，用于在没有参考数据时评估CGI质量的方法，这很可能是实践中最常见的情况。我们的分类法包括11种质量评估方法，这些方法是通过系统的文献综述来确定的。详细介绍了这些方法，包括其主要特征和局限性。这种分类法不仅为现有的CGI质量评估方法提供了系统，全面的说明，而且还使研究人员能够研究各种来源的CGI质量（例如，