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

Authors Yves Annanias , Daniel Wiegreffe , Andreas Niekler , Marta Kuźma , Francis Harvey

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Author Details

Yves Annanias
  • Image and Signal Processing Group, Leipzig University, Germany
Daniel Wiegreffe
  • Image and Signal Processing Group, Leipzig University, Germany
Andreas Niekler
  • Computational Humanities, Leipzig University, Germany
Marta Kuźma
  • Faculty of History, University of Warsaw, Poland
Francis Harvey
  • Leibniz Institute for Regional Geography, Leipzig, Germany
  • Faculty of History, University of Warsaw, Poland

Funding

Marta Kuźma and Francis Harvey: The project is co-financed by the Polish National Agency for Academic Exchange within the NAWA Chair programme.
  • Annanias, Yves: This research was supported by the Development Bank of Saxony (SAB) under Grant 100400221.

Cite AsGet BibTex

Yves Annanias, Daniel Wiegreffe, Andreas Niekler, Marta Kuźma, and Francis Harvey. Development of a Semantic Segmentation Approach to Old-Map Comparison (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 14:1-14:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.GIScience.2023.14

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.

Subject Classification

ACM Subject Classification
  • Human-centered computing → Interactive systems and tools
  • Information systems → Geographic information systems
Keywords
  • Geographic/Geospatial Visualization
  • Visual Knowledge Discovery
  • Cartographic Analysis

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References

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