LIPIcs.GIScience.2023.30.pdf
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Progress in Constructing an Open Map Generalization Data Set for Deep Learning (Short Paper)
Authors
Cheng Fu 
,
Zhiyong Zhou ,
Jan Winkler ,
Robert Weibel
-
Part of:
Volume:
12th International Conference on Geographic Information Science (GIScience 2023)
Series: Leibniz International Proceedings in Informatics (LIPIcs)
Conference: International Conference on Geographic Information Science (GIScience) - License:
Creative Commons Attribution 4.0 International license
- Publication Date: 2023-09-07
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Document Identifiers
Author Details
- Department of Environmental Systems Science, Swiss Federal Institute of Technology, Zürich, Switzerland
Acknowledgements
We would like to thank Roman Geisthoevel at swisstopo for his kind support and helpful discussion.
Cite AsGet BibTex
Cheng Fu, Zhiyong Zhou, Jan Winkler, Nicolas Beglinger, and Robert Weibel. Progress in Constructing an Open Map Generalization Data Set for Deep Learning (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 30:1-30:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.GIScience.2023.30
https://doi.org/10.4230/LIPIcs.GIScience.2023.30
Abstract
Recent pioneering works have shown the potential of a new deep-learning-backed paradigm for automated map generalization. However, this approach also puts a high demand on the availability of balanced and rich training sets. We present our design and progress of constructing an open training data set that can support relevant studies, collaborating with the Swiss Federal Office of Topography. The proposed data set will contain transitions of building and road generalization in Swiss maps at 1:25k, 1:50k, and 1:100k. By analyzing the generalization operators involved in these transitions, we also propose several challenges that can benefit from our proposed data set. Besides, we hope to also stimulate the production of further open data sets for deep-learning-backed map generalization.
Subject Classification
ACM Subject Classification
- Information systems → Geographic information systems
- Information systems → Data mining
- Information systems → Document structure
Keywords
- open data
- deep learning
- map generalization
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