License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.GISCIENCE.2018.66
URN: urn:nbn:de:0030-drops-93941
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2018/9394/
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Xing, Jin ; Sieber, Renee E.

Propagation of Uncertainty for Volunteered Geographic Information in Machine Learning (Short Paper)

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Abstract

Although crowdsourcing drives much of the interest in Machine Learning (ML) in Geographic Information Science (GIScience), the impact of uncertainty of Volunteered Geographic Information (VGI) on ML has been insufficiently studied. This significantly hampers the application of ML in GIScience. In this paper, we briefly delineate five common stages of employing VGI in ML processes, introduce some examples, and then describe propagation of uncertainty of VGI.

BibTeX - Entry

@InProceedings{xing_et_al:LIPIcs:2018:9394,
  author =	{Jin Xing and Renee E. Sieber},
  title =	{{Propagation of Uncertainty for Volunteered Geographic Information in Machine Learning (Short Paper)}},
  booktitle =	{10th International Conference on Geographic Information  Science (GIScience 2018)},
  pages =	{66:1--66:6},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-083-5},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{114},
  editor =	{Stephan Winter and Amy Griffin and Monika Sester},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2018/9394},
  URN =		{urn:nbn:de:0030-drops-93941},
  doi =		{10.4230/LIPIcs.GISCIENCE.2018.66},
  annote =	{Keywords: Uncertainty, Machine Learning, Volunteered Geographic Information, Uncertainty Propagation}
}

Keywords: Uncertainty, Machine Learning, Volunteered Geographic Information, Uncertainty Propagation
Collection: 10th International Conference on Geographic Information Science (GIScience 2018)
Issue Date: 2018
Date of publication: 02.08.2018

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