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Article

A Rule-Based Spatial Reasoning Approach for OpenStreetMap Data Quality Enrichment; Case Study of Routing and Navigation

GIScience research group, Institute of Geography, Heidelberg University, Im Neuenheimer Feld 348, 69120 Heidelberg, Germany
Sensors 2017, 17(11), 2498; https://doi.org/10.3390/s17112498
Received: 4 August 2017 / Revised: 26 September 2017 / Accepted: 12 October 2017 / Published: 31 October 2017
(This article belongs to the Special Issue Crowd-Sensing and Remote Sensing Technologies for Smart Cities)
Finding relevant geospatial information is increasingly critical because of the growing volume of geospatial data available within the emerging “Big Data” era. Users are expecting that the availability of massive datasets will create more opportunities to uncover hidden information and answer more complex queries. This is especially the case with routing and navigation services where the ability to retrieve points of interest and landmarks make the routing service personalized, precise, and relevant. In this paper, we propose a new geospatial information approach that enables the retrieval of implicit information, i.e., geospatial entities that do not exist explicitly in the available source. We present an information broker that uses a rule-based spatial reasoning algorithm to detect topological relations. The information broker is embedded into a framework where annotations and mappings between OpenStreetMap data attributes and external resources, such as taxonomies, support the enrichment of queries to improve the ability of the system to retrieve information. Our method is tested with two case studies that leads to enriching the completeness of OpenStreetMap data with footway crossing points-of-interests as well as building entrances for routing and navigation purposes. It is concluded that the proposed approach can uncover implicit entities and contribute to extract required information from the existing datasets. View Full-Text
Keywords: data mining; OpenStreetMap; data quality enrichment; routing; crowdsourced geographic information; VGI data mining; OpenStreetMap; data quality enrichment; routing; crowdsourced geographic information; VGI
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MDPI and ACS Style

Mobasheri, A. A Rule-Based Spatial Reasoning Approach for OpenStreetMap Data Quality Enrichment; Case Study of Routing and Navigation. Sensors 2017, 17, 2498. https://doi.org/10.3390/s17112498

AMA Style

Mobasheri A. A Rule-Based Spatial Reasoning Approach for OpenStreetMap Data Quality Enrichment; Case Study of Routing and Navigation. Sensors. 2017; 17(11):2498. https://doi.org/10.3390/s17112498

Chicago/Turabian Style

Mobasheri, Amin. 2017. "A Rule-Based Spatial Reasoning Approach for OpenStreetMap Data Quality Enrichment; Case Study of Routing and Navigation" Sensors 17, no. 11: 2498. https://doi.org/10.3390/s17112498

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