Methods, algorithms, and programs for error detection in a geospatial database: a case study of the state land cadastre

Maintaining large geospatial databases involves significant risks of errors, which can negatively impact data accuracy and subsequent usage processes. The primary causes of er-rors include human factors, technical inaccuracies in data integration, and discrepancies in storage projections. With the continuous growth of data volume, manual verification methods become inefficient, necessitating the development of automated error detection methods. The aim of this study is to develop and validate algorithms for error detection using the land parcel database of the State Land Cadastre of Ukraine as a case study. The research re-sults demonstrate the effectiveness of spatial queries, such as point-in-polygon and polygon-in-polygon, in detecting incorrect object placement. The proposed algorithms help reduce the number of errors by automatically controlling the accuracy of data entry. Implementing au-tomated verification mechanisms will enhance the quality of cadastral data, which is crucial for urban planning and land resource management.