Cadastral Positioning Accuracy Improvement (Pai): A Case Study of Pre-Requisite Data Quality Assurance

Abstract

Abstract. Nowadays, there is an increasing need for comprehensive spatial data management especially digital cadastral database (DCDB). Previously, the cadastral database is in hard copy map, then converted into digital format and subsequently updated. Theoretically, these legacy datasets have relatively low positional accuracy caused by limitation of traditional measurement, adjustment technique and technology changes over time. With the growth of spatial based technology especially Geographical Information System (GIS) and Global Navigation Satellite System (GNSS) the Positional Accuracy Improvement (PAI) to the legacy cadastral database is inevitable. PAI is the refining process of the geometry feature in a geospatial dataset through integration between legacy and higher accuracy dataset to improve its actual position. However, by merely integrating both datasets will lead to a distortion of the relative geometry. Thus, an organized method is required to minimize inherent errors in fitting to the new accurate dataset. The focus of this study is to design a comprehensive data preparation for legacy cadastral datasets improvement. The elements of datum traceability, cadastral error propagation and weightage setting in adjustment will be focused to achieve the targeted objective. The proposed result can be applied as a foundation for PAI approach in cadastral database modernization.