G. Molnár

Search for other papers by G. Molnár in
Current site
Google Scholar
Restricted access

A method is presented in this paper for solving a practical problem: how to make georeferenced mosaic of a map series using ground control points and quadratic polynomial transformation for every individual map sheet, if we expect, that after georeferencing the edges of the transformed map sheets should fit together.To solve this problem we can use a constrained polynomial fit method. In this method we use least square adjustment to get the transformation parameters for every individual map sheets, and we define constrains, that the common edges of every two neighboring map sheet should transform similarly. Solving this equation we get the transformation parameters for every single map sheet. Using these parameters for transforming the map sheets, we get georeferenced maps, that automatically fit together in a GIS software.This method has been successfully applied for georeferencing 18 map sheets of the First Topographic Survey of the Habsburg Empire. The resulting georeferenced map has larger residual errors than the individually transformed map sheets, but in exchange for we get a seamless map mosaic, that is more accurate, than the graphically merged and transformed.

  • Collapse
  • Expand

To see the editorial board, please visit the website of Springer Nature.

Manuscript Submission: HERE

For subscription options, please visit the website of Springer Nature.

Acta Geodaetica et Geophysica
Language English
Size B5
Year of
per Year
per Year
Founder Magyar Tudományos Akadémia
H-1051 Budapest, Hungary, Széchenyi István tér 9.
Publisher Akadémiai Kiadó Springer
Nature Switzerland AG
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
CH-6330 Cham, Switzerland Gewerbestrasse 11.
Chief Executive Officer, Akadémiai Kiadó
ISSN 2213-5812 (Print)
ISSN 2213-5820 (Online)

Monthly Content Usage

Abstract Views Full Text Views PDF Downloads
Jun 2023 5 0 0
Jul 2023 10 0 0
Aug 2023 3 0 1
Sep 2023 3 0 0
Oct 2023 4 1 0
Nov 2023 7 4 0
Dec 2023 0 0 0