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Accuracy of Determination of Corresponding Points from Available Providers of Spatial Data—A Case Study from Slovakia

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  • Slavomir Labant

    (Institute of Geodesy, Cartography and Geographical Information Systems, Faculty of Mining, Ecology, Process Control and Geotechnology, Technical University of Kosice, 04200 Kosice, Slovakia)

  • Patrik Petovsky

    (Institute of Geodesy, Cartography and Geographical Information Systems, Faculty of Mining, Ecology, Process Control and Geotechnology, Technical University of Kosice, 04200 Kosice, Slovakia)

  • Pavel Sustek

    (Department of Geodesy and Mine Surveying, Faculty of Mining and Geology, VSB—Technical University of Ostrava, 70833 Ostrava, Czech Republic)

  • Lubomir Leicher

    (Department of Geodesy and Mine Surveying, Faculty of Mining and Geology, VSB—Technical University of Ostrava, 70833 Ostrava, Czech Republic)

Abstract

Mapping the terrain and the Earth’s surface can be performed through non-contact methoYes, that is correct.ds such as laser scanning. This is one of the most dynamic and effective data collection methods. This case study aims to analyze the usability of spatial data from available sources and to choose the appropriate solutions and procedures for processing the point cloud of the area of interest obtained from available web applications. The processing of the point cloud obtained by airborne laser scanning results in digital terrain models created in selected software. The study also included modeling of different types of residential development, and the results were evaluated. Different data sources may have compatibility issues, which means that the position of the same object from different spatial data databases may not be identical. To address this, deviations of the corresponding points were determined from various data sources such as Real Estate Cadaster, ZBGIS Buildings, LiDAR point cloud, orthophoto mosaic, and geodetic measurements. These deviations were analyzed according to their size and orientation, with the average deviations ranging from 0.22 to 0.34 m and standard deviations from 0.11 to 0.20 m. The Real Estate Cadaster was used as the correct basis for comparison. The area of the building was also compared, with the slightest difference being present between the Real Estate Cadaster and geodetic measurement. The difference was zero after rounding the area to whole numbers. The maximum area difference was +5 m 2 for ZBGIS Buildings.

Suggested Citation

  • Slavomir Labant & Patrik Petovsky & Pavel Sustek & Lubomir Leicher, 2024. "Accuracy of Determination of Corresponding Points from Available Providers of Spatial Data—A Case Study from Slovakia," Land, MDPI, vol. 13(6), pages 1-18, June.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:6:p:875-:d:1416776
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    References listed on IDEAS

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    1. Malof, Jordan M. & Bradbury, Kyle & Collins, Leslie M. & Newell, Richard G., 2016. "Automatic detection of solar photovoltaic arrays in high resolution aerial imagery," Applied Energy, Elsevier, vol. 183(C), pages 229-240.
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