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.2023.30
URN: urn:nbn:de:0030-drops-189257
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2023/18925/
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Fu, Cheng ; Zhou, Zhiyong ; Winkler, Jan ; Beglinger, Nicolas ; Weibel, Robert

Progress in Constructing an Open Map Generalization Data Set for Deep Learning (Short Paper)

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LIPIcs-GIScience-2023-30.pdf (2 MB)

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.

BibTeX - Entry

@InProceedings{fu_et_al:LIPIcs.GIScience.2023.30,
  author =	{Fu, Cheng and Zhou, Zhiyong and Winkler, Jan and Beglinger, Nicolas and Weibel, Robert},
  title =	{{Progress in Constructing an Open Map Generalization Data Set for Deep Learning}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{30:1--30:6},
  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/2023/18925},
  URN =		{urn:nbn:de:0030-drops-189257},
  doi =		{10.4230/LIPIcs.GIScience.2023.30},
  annote =	{Keywords: open data, deep learning, map generalization}
}

Keywords: open data, deep learning, map generalization
Collection: 12th International Conference on Geographic Information Science (GIScience 2023)
Issue Date: 2023
Date of publication: 07.09.2023

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