Georeferencing Historic Map Series: An Automated Approach

Student Report (2023)
Authors

E.C. Tsipa (TU Delft - Architecture and the Built Environment)

G. Iliopoulos (TU Delft - Architecture and the Built Environment)

O.J. Post (TU Delft - Architecture and the Built Environment)

R.M. Aalders (TU Delft - Architecture and the Built Environment)

Supervisors

Edward Verbree (TU Delft - Digital Technologies)

Faculty
Architecture and the Built Environment, Architecture and the Built Environment
Copyright
© 2023 Eirini Tsipa, Giorgos Iliopoulos, Oliver Post, Rianne Aalders
More Info
expand_more
Publication Year
2023
Language
English
Copyright
© 2023 Eirini Tsipa, Giorgos Iliopoulos, Oliver Post, Rianne Aalders
Graduation Date
16-11-2023
Awarding Institution
Delft University of Technology
Project
GEO1011 Synthesis Project
Programme
Geomatics
Related content

The code created for and used in this project

https://github.com/geor-tudelft/iiifmap

The client of our project and the annotation pages we created can be viewed in its viewer

https://allmaps.org/
Faculty
Architecture and the Built Environment, Architecture and the Built Environment
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

The present report is the end result of the project that was carried out as part of the Geomatics Synthesis Project in cooperation with AllMaps, an open-source platform dedicated to the viewing and georeferencing of historic maps. The main objective of the project was to automatically georeference historic map series curated and digitised by the Dutch National Archive. This was based on the  corner coordinates of the map sheets. The first issue that had to be tackled was the reprojection of the original coordinates which were in Bonne projection
to WGS84 coordinates. To determine the corners of the map content within the sheets two methods were implemented. The first one detects the lines based on HoughLines Probabilistic Transformation and the second one detects lines based on the distribution of black pixels in the rows and columns of the images. In addition to map sheets with corner coordinates, there are two other sets of images which were georeferenced utilising a convolution neural network that performs feature matching. The feature matching was performed by running the two sets of images against the georeferenced sheets with known corner coordinates. To minimise the search space for this process a geocoder was used to determine the approximate location of the image. The implemented methods appear to hold the potential for georeferencing old map series. It is worth noting that the developed algorithms, while effective in many cases, may encounter challenges when dealing with irregularities on map sheets caused by the passage
of time, such as damage. Consequently, there is a great opportunity to further enhance the algorithms to ensure they can consistently and accurately georeference images, even when faced with such irregularities. This ongoing development will lead to improved georeferencing accuracy and user confidence.

Files

Project5_Final_Report_1.pdf
(pdf | 9.07 Mb)
License info not available