This research addresses the critical issue of open geospatial data quality, focusing on its relevance for topographic and geodetic applications, urban planning, and the development of national spatial data infrastructures. The authors developed a comprehensive methodology to assess three key quality dimensions: positional accuracy, completeness, and level of detail across five popular datasets: OpenStreetMap, Microsoft Bing Building Footprints, Google Satellite, Bing Maps, and Esri Imagery. Geospatial and statistical analyses were conducted using digital topographic maps at a 1:2000 scale as reference data for selected settlements in Ukraine. The results revealed significant variability among the datasets. Microsoft Bing Building Footprints demonstrated the highest completeness, while Google Satellite and Bing Maps achieved the best results in terms of detail. However, all datasets showed notable discrepancies in positional accuracy compared to reference data, rendering automated point-to-point matching ineffective. The findings underscore the need for rigorous quality assessment when using open geospatial data, particularly in applications where data reliability directly affects decision-making outcomes and public safety.
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