License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.GIScience.2023.14
URN: urn:nbn:de:0030-drops-189099
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2023/18909/
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Annanias, Yves ; Wiegreffe, Daniel ; Niekler, Andreas ; Kuźma, Marta ; Harvey, Francis

Development of a Semantic Segmentation Approach to Old-Map Comparison (Short Paper)

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LIPIcs-GIScience-2023-14.pdf (2 MB)

Abstract

This paper describes an innovative computational approach for comparing old maps. Maps older than 20 years remain a vast treasure of geographic information in many parts of the world with potential applications in many environmental and social analyses, e.g., establishing road construction over the past 80 years or identifying settlement growth since the middle ages. Semantic segmentation has developed into a viable computational method for analysing old maps from previous centuries. It allows for the discrete identification of elements, e.g., lakes, forests, and roads, from cartographic sources and their computational modelling. Semantic segmentation uses convolutional neural networks to extract elements. With this technique, we create a computational approach to compare old maps systematically and efficiently.

BibTeX - Entry

@InProceedings{annanias_et_al:LIPIcs.GIScience.2023.14,
  author =	{Annanias, Yves and Wiegreffe, Daniel and Niekler, Andreas and Ku\'{z}ma, Marta and Harvey, Francis},
  title =	{{Development of a Semantic Segmentation Approach to Old-Map Comparison}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{14:1--14:6},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-288-4},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{277},
  editor =	{Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2023/18909},
  URN =		{urn:nbn:de:0030-drops-189099},
  doi =		{10.4230/LIPIcs.GIScience.2023.14},
  annote =	{Keywords: Geographic/Geospatial Visualization, Visual Knowledge Discovery, Cartographic Analysis}
}

Keywords: Geographic/Geospatial Visualization, Visual Knowledge Discovery, Cartographic Analysis
Collection: 12th International Conference on Geographic Information Science (GIScience 2023)
Issue Date: 2023
Date of publication: 07.09.2023

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