International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Download
Citation
Volume XLII-4/W16
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W16, 255–260, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W16-255-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W16, 255–260, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W16-255-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  01 Oct 2019

01 Oct 2019

CADASTRAL POSITIONING ACCURACY IMPROVEMENT (PAI): A CASE STUDY OF PRE-REQUISITE DATA QUALITY ASSURANCE

N. M. Hashim1,3, A. H. Omar1, K. M. Omar2, M. A. Abbas3, M. A. Mustafar3, and S. A. Sulaiman3 N. M. Hashim et al.
  • 1Geomatic Innovation Research Group (GnG), Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia, Johor Bahru, Malaysia
  • 2Geospatial Science and Technology College, Kuala Lumpur, Malaysia
  • 3Centre of Studies for Surveying Sciences & Geomatics, Faculty of Architecture, Planning & Surveying, Universiti Teknologi Mara Perlis, Arau, Malaysia

Keywords: Positional Accuracy Improvement, Legacy Dataset; Cadastral Database Modernization

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.