2026-08-11T00:06:52Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:9394 2024-03-06T10:44:13Z ddc:004 open_access

Propagation of Uncertainty for Volunteered Geographic Information in Machine Learning (Short Paper) Xing, Jin Sieber, Renee E. Uncertainty Machine Learning Volunteered Geographic Information Uncertainty Propagation 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. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Jin Xing and Renee E. Sieber 2018 Is Part Of LIPIcs, Volume 114, 10th International Conference on Geographic Information Science (GIScience 2018) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.GISCIENCE.2018.66 urn:nbn:de:0030-drops-93941 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GISCIENCE.2018.66 eng https://creativecommons.org/licenses/by/3.0/legalcode