Spatial Accuracy Assessment of Buildings in Openstreetmap

Yıl 2020, Cilt: 7 Sayı: 2, 205 - 212, 15.08.2020

Kadir Küçük , Berk Anbaroğlu

https://doi.org/10.30897/ijegeo.710508

Cited By: 1

Öz

The aim of this paper is to assess the spatial accuracy of OpenStreetMap (OSM) with respect to the Turkey Topographic Vector Database (TOPOVT) within the context of ‘building’ layer. Being an open-platform, anyone can access to OSM and add geographic entities as well as update them. Since there is no stringent standards, spatial accuracy assessment of OSM is an open research area. TOPOVT, on the other hand, is produced by the General Directorate of Mapping by following a standard procedure, where the maps are produced for 1:25000 scale or larger scale. Updating this database is a costly process and could only be conducted at specific time intervals. Therefore, automatic detection of the locations requiring update in TOPOVT would be an effective operation, which would eventually reduce the overall cost of the database update. However, the spatial accuracy of the geographical features have to be analysed in order to support such a motivation. Therefore, the aim of this paper is to assess the spatial accuracy of ‘building’ layer by calculating the Hausdorff distance between the matching (homologous) polygons in OSM and TOPOVT. The proposed methodology consists of two methods to detect the matching polygons: ‘overlap method’ and ‘centroid method’. Hausdorff distance is calculated for only those intersecting buildings in both of the layers. Since it is safe to assume that the intersecting polygons refer to the same geographic object, the calculated distance could be used to indicate the spatial accuracy of the building. The developed software is tested on an urban and a rural environment in Ankara, Turkey. The results indicate that the quality of OSM could well match with TOPOVT. Specifically, the average Hausdorff distance is approximately the same for both of the methods: approximately 9.5 metres. Considering that OSM and TOPOVT are generated through completely different processes’, the spatial accuracy is considered to be ‘good’ and ‘useful’ for many practical and operational purposes. In order to increase the effectiveness of the developed methodology in a real-life context, the whole process is integrated into an ArcMap extension and the code is made available on GitHub.

Anahtar Kelimeler

Hausdorff distance, volunteered geographic information, TOPOVT, spatial analysis, GIS

Teşekkür

The authors would like to thank General Directorate of Mapping for providing the TOPOVT data. In addition, the authors are also grateful for the feedback provided by Altan Yılmaz and Mustafa Canıberk. The contents of this paper reflect the views of the authors, who are responsible for the facts and the accuracy of the results presented herein.

Kaynakça

• Avbelj, J., Müller, R., Bamler, R., 2015. A Metric for Polygon Comparison and Building Extraction Evaluation. IEEE Geoscience and Remote Sensing Letters 12, 170–174.
• Barron, C., Neis, P., Zipf, A., 2014. A Comprehensive Framework for Intrinsic OpenStreetMap Quality Analysis. Transactions in GIS 18, 877–895.
• Brovelli, M.A., Zamboni, G., 2018. A New Method for the Assessment of Spatial Accuracy and Completeness of OpenStreetMap Building Footprints. ISPRS International Journal of Geo-Information 7, 289.
• Çabuk, S., Erdoğan, M., Önal, E., 2015. Open Street Map Verilerinden Yararlanılarak 1/50K Ölçekli Harita Üretilebilirliğinin Araştırılması. Harita Dergisi 26–34.
• Fan, H., Zipf, A., Fu, Q., Neis, P., 2014. Quality assessment for building footprints data on OpenStreetMap. International Journal of Geographical Information Science 28, 700–719.
• Feick, R., Roche, S., 2013. Understanding the Value of VGI, in: Sui, D., Elwood, S., Goodchild, M. (Eds.), Crowdsourcing Geographic Knowledge. Springer Netherlands, pp. 15–29.
• Goodchild, M.F., 2007. Citizens as sensors: the world of volunteered geography. GeoJournal 69, 211–221.
• Gupta, S., Pebesma, E., Degbelo, A., Costa, A.C., 2018. Optimising Citizen-Driven Air Quality Monitoring Networks for Cities. ISPRS International Journal of Geo-Information 7, 468.
• Hachmann, S., Jokar Arsanjani, J., Vaz, E., 2018. Spatial data for slum upgrading: Volunteered Geographic Information and the role of citizen science. Habitat International, Regional Intelligence: A new kind of GIScience 72, 18–26.
• Haklay, M., 2013. Citizen Science and Volunteered Geographic Information: Overview and Typology of Participation, in: Sui, D., Elwood, S., Goodchild, M. (Eds.), Crowdsourcing Geographic Knowledge: Volunteered Geographic Information (VGI) in Theory and Practice. Springer
• Haklay, M., 2010. How Good is Volunteered Geographical Information? A Comparative Study of OpenStreetMap and Ordnance Survey Datasets. Environ Plann B Plann Des 37, 682–703.
• Haklay, M. (Muki), Basiouka, S., Antoniou, V., Ather, A., 2010. How Many Volunteers Does it Take to Map an Area Well? The Validity of Linus’ Law to Volunteered Geographic Information. The Cartographic Journal 47, 315–322.
• Hecht, R., Kunze, C., Hahmann, S., 2013. Measuring Completeness of Building Footprints in OpenStreetMap over Space and Time. ISPRS International Journal of Geo-Information 2, 1066–1091.
• Küçük, 2019. Hausdorff Analysis. Contribute to kadirkucuk/Proje development by creating an account on GitHub. https://github.com/kadirkucuk/Proje (16 April 2019)
• OSM, 2019. OpenStreetMap Copyright and Licence. https://www.openstreetmap.org/copyright/en (16 April 2019)
• QGIS, 2019. OpenStreetMap Verisinde Arama Yapma ve Veriyi İndirme — QGIS Tutorials and Tips. http://www.qgistutorials.com/tr/docs/downloading_osm_data.html (16 April 2019)
• Qi, Y., Zhang, C., Zhi, Z., Guo, K., Guo, D., 2018. A VGI-based Foodborn Disease Report and Forecast System, in: Proceedings of the 4th ACM SIGSPATIAL International Workshop on Safety and Resilience, Safety and Resilience’18. ACM, New York, NY, USA, pp. 18:1–18:7.
• Schlesinger, M.I., Vodolazskii, Y.V., Yakovenko, V.M., 2014. Recognizing the Similarity of Polygons in a Strengthened Hausdorff Metric. Cybern Syst Anal 50, 476–486.
• Senaratne, H., Mobasheri, A., Ali, A.L., Capineri, C., Haklay, M. (Muki), 2017. A review of volunteered geographic information quality assessment methods. International Journal of Geographical Information Science 31, 139–167.
• Sevinç, H.K., Karaş, İ.R., 2018. Gönüllü Coğrafi Bilgi, Sivil Bilim ve Katılımcı Coğrafi Bilgi Sistemleri Arasındaki Benzerlikler ve Farklılıklar.
• Taşkanat, T., Karaağaç, A., Beşdok, E., Bostanci, B., 2018. Kentsel Sorunların Yönetimi için Bir Gönüllü Coğrafi Bilgi Mobil Uygulaması Geliştirilmesi. Geomatik 3, 84–91.
• Touya, G., Antoniou, V., Olteanu-Raimond, A.-M., Van Damme, M.-D., 2017. Assessing Crowdsourced POI Quality: Combining Methods Based on Reference Data, History, and Spatial Relations. ISPRS International Journal of Geo-Information 6, 80.
• USGS, 2019. Volunteered Geographic Information (VGI). https://www.usgs.gov/core-science-systems/ngp/cegis/vgi (16 April 2019) Venegin, H., 1999. Data quality parameters, in: Geographical Information Systems: Principles and Technical Issues. John wiley and Sons, pp. 177–89.
• Yılmaz, A., Canıberk, M., 2018. Real Time Vector Database Updating System: A Case Study for Turkish Topographic Vector Database (TOPOVT). International Journal of Engineering and Geosciences 3, 73–79.