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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自愿和众包地理信息质量评估方法的分类。


众包地理信息 (CGI) 的使用日益广泛，促使采用多种评估信息质量的方法，旨在解决对非专家提供的信息质量缺乏的担忧。在这项工作中，我们提出了一种在没有参考数据的情况下评估 CGI 质量的方法分类，这可能是实践中最常见的情况。我们的分类法包括通过系统文献综述确定的 11 种质量评估方法。详细描述了这些方法，包括它们的主要特征和局限性。该分类不仅系统、全面地描述了现有的 CGI 质量评估方法集，而且使研究各种来源（例如社交媒体、人群感知、协作制图）的 CGI 质量的研究人员能够从各个来源中学习其他，从而为未来的工作开辟了途径，将现有方法组合并扩展到新的应用领域和领域。