The reliability and quality of volunteered geographic information (VGI) continue to be pressing concerns. Many VGI projects lack standard geospatial data quality assurance procedures, and the reliability of contributors remains in question. Traditional approaches rely on comparing VGI to an “authoritative” or “gold standard” dataset to assess quality. This study investigates VGI quality by analysing the OpenStreetMap (OSM) database in Ottawa‐Gatineau, focusing on historical map features and contributor data to gain an understanding of how users are contributing to the database, and their ability to do so accurately. Unsupervised machine learning analyses expose a cluster of experienced contributors classified as “OSM validators/experts”, which are then further used to attribute data quality. They are identified through a combination of strong contribution loadings associated with the use and experience of advanced OSM editors, and weaker loadings associated with feature creation and frequency of contributions leading to further correction. Limitations are discussed with implications for future work.

中文翻译：

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使用无监督机器学习方法的OpenStreetMap质量评估

自愿性地理信息（VGI）的可靠性和质量继续成为人们关注的焦点。许多VGI项目缺乏标准的地理空间数据质量保证程序，并且贡献者的可靠性仍然存在疑问。传统方法依靠将VGI与“权威”或“黄金标准”数据集进行比较以评估质量。这项研究通过分析渥太华加蒂诺（Ottawa‐Gatineau）的OpenStreetMap（OSM）数据库来调查VGI的质量，重点关注历史地图特征和贡献者数据，以了解用户如何对数据库做出贡献以及他们准确地做到这一点的能力。无监督的机器学习分析暴露了经验丰富的贡献者群体，这些贡献者被归类为“ OSM验证者/专家”，然后被进一步用来归因于数据质量。通过与高级OSM编辑器的使用和经验相关的强大贡献负载，以及与功能创建和贡献频率相关的较弱负载（可导致进一步校正）的组合来识别它们。讨论了局限性，对将来的工作有影响。