Next Article in Journal
Ionizing Radiation Measurement Solution in a Hospital Environment
Next Article in Special Issue
3VSR: Three Valued Secure Routing for Vehicular Ad Hoc Networks using Sensing Logic in Adversarial Environment
Previous Article in Journal
H∞ Robust Control of a Large-Piston MEMS Micromirror for Compact Fourier Transform Spectrometer Systems
Previous Article in Special Issue
Adaptive Sampling for Urban Air Quality through Participatory Sensing
Open AccessArticle

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

GIScience Research Group, Institute of Geography, Heidelberg University, 69120 Heidelberg, Germany
GIScience Center of the Department of Geography, University of Zurich (UZH), 8057 Zurich, Switzerland
Department of Computer Systems, University of São Paulo, São Carlos 13566-590, Brazil
Author to whom correspondence should be addressed.
Sensors 2018, 18(2), 509;
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)
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
Show Figures

Figure 1

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.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop