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Sensors 2018, 18(2), 509; doi:10.3390/s18020509

Enrichment of OpenStreetMap Data Completeness with Sidewalk Geometries Using Data Mining Techniques

1
GIScience Research Group, Institute of Geography, Heidelberg University, 69120 Heidelberg, Germany
2
GIScience Center of the Department of Geography, University of Zurich (UZH), 8057 Zurich, Switzerland
3
Department of Computer Systems, University of São Paulo, São Carlos 13566-590, Brazil
*
Author to whom correspondence should be addressed.
Received: 16 October 2017 / Revised: 28 January 2018 / Accepted: 29 January 2018 / Published: 8 February 2018
(This article belongs to the Special Issue Crowd-Sensing and Remote Sensing Technologies for Smart Cities)
View Full-Text   |   Download PDF [5757 KB, uploaded 8 February 2018]   |  

Abstract

Tailored routing and navigation services utilized by wheelchair users require certain information about sidewalk geometries and their attributes to execute efficiently. Except some minor regions/cities, such detailed information is not present in current versions of crowdsourced mapping databases including OpenStreetMap. CAP4Access European project aimed to use (and enrich) OpenStreetMap for making it fit to the purpose of wheelchair routing. In this respect, this study presents a modified methodology based on data mining techniques for constructing sidewalk geometries using multiple GPS traces collected by wheelchair users during an urban travel experiment. The derived sidewalk geometries can be used to enrich OpenStreetMap to support wheelchair routing. The proposed method was applied to a case study in Heidelberg, Germany. The constructed sidewalk geometries were compared to an official reference dataset (“ground truth dataset”). The case study shows that the constructed sidewalk network overlays with 96% of the official reference dataset. Furthermore, in terms of positional accuracy, a low Root Mean Square Error (RMSE) value (0.93 m) is achieved. The article presents our discussion on the results as well as the conclusion and future research directions. View Full-Text
Keywords: sidewalk; routing; open data; OpenStreetMap; data quality; completeness sidewalk; routing; open data; OpenStreetMap; data quality; completeness
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Mobasheri, A.; Huang, H.; Degrossi, L.C.; Zipf, A. Enrichment of OpenStreetMap Data Completeness with Sidewalk Geometries Using Data Mining Techniques. Sensors 2018, 18, 509.

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