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Homogeneous Coordinate Frames vs. Existing Inhomogeneous Data

Homogeneous Coordinate Frames vs. Existing Inhomogeneous Data. Gerhard Navratil 1 Ismail Kabashi 2,3 Michaela Ragoßnig-Angst 3. 1 Vienna University of Technology, Institute for Geoinformation and Cartography 2 University Prstina, Department for Geodesy 3 Vermessung Angst ZT GmbH.

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Homogeneous Coordinate Frames vs. Existing Inhomogeneous Data

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  1. Homogeneous Coordinate Frames vs. Existing Inhomogeneous Data Gerhard Navratil1 Ismail Kabashi2,3 Michaela Ragoßnig-Angst3 1 Vienna University of Technology, Institute for Geoinformation and Cartography 2 University Prstina, Department for Geodesy 3 Vermessung Angst ZT GmbH

  2. Contents • The Problem • Example Austria • Solutions • Discussion Navratil, Kabashi, Ragoßnig-Angst

  3. The Problem Paradigm change in surveying:local to global observations Many administrative datasets based on local observations New datasets based on GNSS  Inconsistencies How to solve them? Navratil, Kabashi, Ragoßnig-Angst

  4. I Example Austria (1) • Reference frame started in 1862 (complete survey) • Triangulation network • Adjusted in parts (constrained a.) • Today ETRF-coordinates available • Cadastre started in 1817 • Different triangulation, measurement table • Since 1969 coordinate-based definition Navratil, Kabashi, Ragoßnig-Angst

  5. Example Austria (2) Problem for engineering projects (streets, power supply lines, etc.) (Höggerl & Imrek 2007) Navratil, Kabashi, Ragoßnig-Angst

  6. c a b 2 1 e d 3 Solutions (1) • Simple Helmert/affine transformation • Residual-free affine transformation • Multi-quadratic interpolationuj=sjTS-1r Navratil, Kabashi, Ragoßnig-Angst

  7. Solutions (2) • Exact implementationEvaluate a function at a position SLOW • Approximate implementationPrecompute for a grid, interpolate faster, but repeated change between systems may change the coordinates Navratil, Kabashi, Ragoßnig-Angst

  8. Discussion • Significant problem for engineers and SDIs • Methods are available to deal with inhomogeneous datasets • Methods may require adaption of data manipulation processes Navratil, Kabashi, Ragoßnig-Angst

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