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. 2018 Feb 8;18(2):509.
doi: 10.3390/s18020509.

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

Amin Mobasheri  1 , Haosheng Huang  2 , Lívia Castro Degrossi  3 , Alexander Zipf  4
Affiliations

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

Amin Mobasheri et al. Sensors (Basel). .

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.

Keywords: OpenStreetMap; completeness; data quality; open data; routing; sidewalk.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Workflow of the methodology.
Figure 2
Figure 2
The 12-direction chain code (a); and an example (b), adopted from [38].
Figure 3
Figure 3
Part of Heidelberg old town where the experiment took place (map: © OpenStreetMap contributors).
Figure 4
Figure 4
The navigation experiment: (a) a wheelchair user checking the navigation plan; (b) two GPS devices installed on wheelchair; and (c) GPS device worn by a user.
Figure 5
Figure 5
Raw GPS data before preprocessing—1559 points, and 287 GPS points counted as noise (in red circles).
Figure 6
Figure 6
GPS points that shape the geometry of sidewalks (dots are clustered points (512), and squares are candidate points (267)).
Figure 7
Figure 7
Workflow for matching and comparison of OpenStreetMap and GPS dataset.
Figure 8
Figure 8
The final selected significant points (123 points).
Figure 9
Figure 9
Comparison of the original (black) and the enhanced (red) constructed sidewalk network.
Figure 10
Figure 10
Results of visual inspection of sidewalk network with Google Maps (before applying the enhancement stage): (a) the overlaid sidewalk network for the whole experiment area; and (b–d) examples of positional inaccuracies.
Figure 11
Figure 11
Results of visual inspection of sidewalk network with Google Maps (after applying the enhancement stage): (a–c) the same areas in Figure 10b–d, respectively.
See this image and copyright information in PMC

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