文章基本信息

标题：Propagation of Uncertainty for Volunteered Geographic Information in Machine Learning (Short Paper)
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作者：Jin Xing ; Renee E. Sieber
期刊名称：LIPIcs : Leibniz International Proceedings in Informatics
电子版ISSN：1868-8969
出版年度：2018
卷号：114
页码：1-6
DOI：10.4230/LIPIcs.GISCIENCE.2018.66
出版社：Schloss Dagstuhl -- Leibniz-Zentrum fuer Informatik
摘要：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.
关键词：Uncertainty; Machine Learning; Volunteered Geographic Information; Uncertainty Propagation