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

Authors Jin Xing , Renee E. Sieber

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Jin Xing
  • Centre for Research in Geomatics, Laval University, Quebec City, Canada
Renee E. Sieber
  • Department of Geography, McGill University, Montreal, Canada

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Jin Xing and Renee E. Sieber. Propagation of Uncertainty for Volunteered Geographic Information in Machine Learning (Short Paper). In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 66:1-66:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)
https://doi.org/10.4230/LIPIcs.GISCIENCE.2018.66

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.

Subject Classification

ACM Subject Classification
  • Information systems → Uncertainty
Keywords
  • Uncertainty
  • Machine Learning
  • Volunteered Geographic Information
  • Uncertainty Propagation

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