Enrichment of OpenStreetMap Data Completeness with Sidewalk Geometries Using Data Mining Techniques
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 in...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-02-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/18/2/509 |
| _version_ | 1811187253013118976 |
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| author | Amin Mobasheri Haosheng Huang Lívia Castro Degrossi Alexander Zipf |
| author_facet | Amin Mobasheri Haosheng Huang Lívia Castro Degrossi Alexander Zipf |
| author_sort | Amin Mobasheri |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T14:00:11Z |
| format | Article |
| id | doaj.art-3c71ac04d455464ca77aca7d41237070 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T14:00:11Z |
| publishDate | 2018-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-3c71ac04d455464ca77aca7d412370702022-12-22T04:20:09ZengMDPI AGSensors1424-82202018-02-0118250910.3390/s18020509s18020509Enrichment of OpenStreetMap Data Completeness with Sidewalk Geometries Using Data Mining TechniquesAmin Mobasheri0Haosheng Huang1Lívia Castro Degrossi2Alexander Zipf3GIScience Research Group, Institute of Geography, Heidelberg University, 69120 Heidelberg, GermanyGIScience Center of the Department of Geography, University of Zurich (UZH), 8057 Zurich, SwitzerlandDepartment of Computer Systems, University of São Paulo, São Carlos 13566-590, BrazilGIScience Research Group, Institute of Geography, Heidelberg University, 69120 Heidelberg, GermanyTailored 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.http://www.mdpi.com/1424-8220/18/2/509sidewalkroutingopen dataOpenStreetMapdata qualitycompleteness |
| spellingShingle | Amin Mobasheri Haosheng Huang Lívia Castro Degrossi Alexander Zipf Enrichment of OpenStreetMap Data Completeness with Sidewalk Geometries Using Data Mining Techniques Sensors sidewalk routing open data OpenStreetMap data quality completeness |
| title | Enrichment of OpenStreetMap Data Completeness with Sidewalk Geometries Using Data Mining Techniques |
| title_full | Enrichment of OpenStreetMap Data Completeness with Sidewalk Geometries Using Data Mining Techniques |
| title_fullStr | Enrichment of OpenStreetMap Data Completeness with Sidewalk Geometries Using Data Mining Techniques |
| title_full_unstemmed | Enrichment of OpenStreetMap Data Completeness with Sidewalk Geometries Using Data Mining Techniques |
| title_short | Enrichment of OpenStreetMap Data Completeness with Sidewalk Geometries Using Data Mining Techniques |
| title_sort | enrichment of openstreetmap data completeness with sidewalk geometries using data mining techniques |
| topic | sidewalk routing open data OpenStreetMap data quality completeness |
| url | http://www.mdpi.com/1424-8220/18/2/509 |
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