{"title":"staty","authors":"H. Bast, P. Brosi, Markus Näther","doi":"10.1145/3397536.3422342","DOIUrl":null,"url":null,"abstract":"We present staty, a browser-based tool for quality assurance of public transit station tagging in OpenStreetMap (OSM). Building on the results of a similarity classifier for these stations, our tool visualizes name tag errors as well as incorrect and/or missing station group relations. Detailed edit suggestions are provided for individual objects. This is done intrinsically without an external ground truth. Instead, the underlying classifier is trained on the OSM data itself. We describe how our tool derives errors and suggestions from station tag similarities and provide experimental results on the OSM data of the United Kingdom, the United States, and a dataset consisting of Germany, Switzerland, and Austria. Our tool can be accessed under https://staty.cs.uni-freiburg.de.","PeriodicalId":233918,"journal":{"name":"Proceedings of the 28th International Conference on Advances in Geographic Information Systems","volume":"98 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 28th International Conference on Advances in Geographic Information Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3397536.3422342","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

Abstract

We present staty, a browser-based tool for quality assurance of public transit station tagging in OpenStreetMap (OSM). Building on the results of a similarity classifier for these stations, our tool visualizes name tag errors as well as incorrect and/or missing station group relations. Detailed edit suggestions are provided for individual objects. This is done intrinsically without an external ground truth. Instead, the underlying classifier is trained on the OSM data itself. We describe how our tool derives errors and suggestions from station tag similarities and provide experimental results on the OSM data of the United Kingdom, the United States, and a dataset consisting of Germany, Switzerland, and Austria. Our tool can be accessed under https://staty.cs.uni-freiburg.de.
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