Data quality assurance for volunteered geographic information

被引：46

作者：

Ali, Ahmed Loai ^{[1
,3
]}

Schmid, Falko ^{[1
,2
]}

机构：

[1] Cognitive Systems Group, University of Bremen

[2] SFB/TR 8 Spatial Cognition, University of Bremen

来源：

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | 2014年 / 8728卷

关键词：

D O I：

10.1007/978-3-319-11593-1_9

中图分类号：

学科分类号：

摘要：

The availability of technology and tools enables the public to participate in the collection, contribution, editing, and usage of geographic information, a domain previously reserved for mapping agencies or companies. The data of Volunteered Geographic Information (VGI) systems, such as OpenStreetMap (OSM), is based on the availability of technology and participation of individuals. However, this combination also implies quality issues related to the data: some of the contributed entities can be assigned to wrong or implausible classes, due to individual interpretation of the submitted data, or due to misunderstanding about available classes. In this paper we propose two methods to check the integrity of VGI data with respect to hierarchical consistency and classification plausibility. These methods are based on constraint checking and machine learning methods. They can be used to check the validity of data during contribution or at a later stage for collaborative manual or automatic data correction. © Springer International Publishing Switzerland 2014.

引用

页码：126 / 141

页数：15

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