Classification of errors in geospatial databases based on the example data of the state land cadastre

Abstract

The article investigates the issue of error classification in geospatial databases using data of the State Land Cadastre of Ukraine. An extended classification of errors is proposed, including attributive, geometric, topological, and contextual errors. Specifically, it examines positioning errors, topological violations, incorrect attributive data, duplication, and data inconsistency. The article emphasizes the importance of automated error detection to ensure the quality of geospatial data. It concludes with the need for further research using statistical methods for anomaly detection. The proposed approaches enhance analysis accuracy and support decision-making in geographic information systems