Volunteered Geographic Information projects like OpenStreetMap which allow accessing and using the raw data, are a treasure trove for investigations - e.g. cultural topics, urban planning, or accessibility of services. Among the concerns are the reliability and accurateness of the data. While it was found that for mainstream topics, like roads or museums, the data completeness and accuracy is very high, especially in the western world, this is not clear for niche topics. Furthermore, many of the analyses are almost one decade old in which the OpenStreetMap-database grew to over nine billion elements. Based on OpenStreetMap-data of wayside crosses and other cross-like objects regional cultural differences and prevalence of the types within Europe, Germany and Bavaria are investigated. For Bavaria, internally and by comparing to an official dataset and other proxies the data completeness, logical consistency, positional, temporal, and thematic accuracy is assessed. Subsequently, the usability for the specific case and to generalize for the use of OpenStreetMap data for niche topics. It is estimated that about one sixth to one third of the crosses located within Bavaria are recorded in the database and positional accuracy is better than 50 metres in most cases. In addition, linguistic features of the inscriptions, the usage of building materials, dates of erection and other details deducible from the dataset are discussed. It is found that data quality and coverage for niche topics exceeds expectations but varies strongly by region and should not be trusted without thorough dissection of the dataset.

翻译：开放街道地图等自愿地理信息项目允许访问和使用原始数据，为文化议题、城市规划或服务可达性等研究提供了宝贵资源。数据可靠性与准确性是其中备受关注的问题。尽管研究表明，在道路、博物馆等主流主题中，数据完整性与准确性极高（尤其在西方国家），但对于小众主题而言，情况尚不明确。此外，许多分析距今已近十年，而开放街道地图数据库已增长至超过90亿个要素。本研究基于开放街道地图中路侧十字架及其他类十字架物的数据，探讨欧洲、德国及巴伐利亚地区文化差异与类型分布。针对巴伐利亚地区，通过内部比较及与官方数据集和其他替代指标的对比，评估了数据完整性、逻辑一致性、位置精度、时间精度及主题精度。随后分析了该特定案例的可用性，并推广至开放街道地图数据在小众主题中的应用。估算结果显示，巴伐利亚地区约六分之一至三分之一的路侧十字架已被收录于数据库，且多数情况下位置精度优于50米。此外，本文讨论了数据集中可推导的铭文语言特征、建筑材料使用、竖立日期等细节。研究发现，小众主题的数据质量与覆盖度超出预期，但存在显著区域性差异，未经深入剖析的此类数据集不可轻信。