Scientific- Research Quarterly of Geographical Data (SEPEHR)

Scientific- Research Quarterly of Geographical Data (SEPEHR)

An assessment on building blocks completion process in the Volunteered Geographic Information (VGI) for using in smart cities

Document Type : Research Paper

Authors
Roghayeh Adabi 1
Rahim Ali Abbaspour 2
Alireza Chehreghan 3
1 MSc Student of GIS, School of Surveying and Geospatial Information Engineering, College of Engineering, University of Tehran, Tehran, Iran
2 Associate professor, School of Surveying and Geospatial Information Engineering, College of Engineering, University of Tehran, Tehran, Iran
3 Assistant professor, Mining Engineering Faculty, Sahand University of Technology, Tabriz, Iran
10.22131/sepehr.2022.251052
Abstract
Extended Abstract
Introduction
In recent years, data has become the life-giving force of developing innovations in smart cities all around the world. The up-to-date, availability, and freeness of this data are the deciding factors in their frequent use in smart city projects. Today, different sources of information on city-related issues are available. They are crucial for driving towards “Smart Cities”. Among these sources is the Open Street Map (OSM) project, which is a free and open-source information repository used in many urban and non-urban-related applications. At present, OSM is used for a wide range of applications, for example, navigation, location-based services, construction of 3D city models, and traffic simulation. In the meantime, building blocks are among the OSM data that plays a key role in urban-related studies. These studies include constructing 3D building models, modeling urban energy systems, and land-use management in smart cities. Regarding the importance of completeness in the quality of spatial data, this study will assess the historical course of building blocks data completeness in OSM.
 
Materials and methods
The 20 districts of the Tehran metropolis have been selected as the study area. This city, with an area of 730 square kilometers and a population of around 8 million people covers the center of Tehran. The main purpose of this study is to present an analysis of the completeness of building block data in the OSM for the Tehran metropolis in 10 years (between 2011 and 2020). To reach this aim, an object-based approach based on object matching was used to assess the completeness parameter.
 
Results and Discussion
The findings of this study demonstrate that during the recent two years, OSM building block data in Tehran increased in terms of the number of features and the completeness of geometric information considerably. The number of data increased from 300 features in 2011 to 40.138 features in 2020, as well as the number of features edited and added to the OSM dataset increased from 38 and 194 in 2011 to 28680 and 10705 in the end of 2020, respectively. The completeness of OSM building block data in Tehran has increased from 0.18% in 2011 to 2.7% in 2020. Moreover, the evaluation of the completeness of OSM data in different regions of Tehran shows that the completeness of all regions of Tehran was less than 1% from 2011 to 2014, and in the last two years, for 12 of 20 regions of Tehran, the completeness is still less than 1%, but for the other eight regions (i.e., the regions no. 1, 2, 4, 5, 11, 15, and 20), which are mostly located in the northern part of Tehran, the completeness has increased. However, the data have many weaknesses in terms of the attribute information completeness.
 
Conclusion
This study has provided a clear view of OSM building block status in Tehran. In addition, it has provided a better view of OSM data in different regions of Tehran. The insights gained from this study can lead toward creating the awareness required to use of these data in various fields of application. It can also assist local and national managers and related organizations to support active regions and encourage inactive regions. This paper represents a potential starting point for many possible future research directions in smart cities, especially in Tehran. Smart cities can conduct similar studies to understand the state of OSM data in their regions, make plans based on the findings, and manage their space more efficiently. To conduct future research, we evaluate the factors affecting the growth and development of OSM data and the efficiency of the OSM data in some smart city applications.
Keywords
Open Street Map
Tehran city
Building blocks
Completeness
Smart city
1- چمنی، علی‌عباس‌پور، چهرقان؛ رحیم، مژگان، علیرضا (2018). تناظریابی عوارض چندضلعی براساس معیار‌های هندسی در مجموعه داده‌های چند‌مقیاسی. نشریه علمی علوم و فنون نقشه‌برداری، 7، 73-87.
2- Alhamwi, A., Medjroubi, W., Vogt, T., & Agert, C. (2017). OpenStreetMap data in modelling the urban energy infrastructure: a first assessment and analysis. Energy Procedia, 142, 1968-1976.‏
3- Biljecki, F. (2020). Exploration of Open Data in Southeast Asia to Generate 3D Building Models. ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, 6.‏
4- Brovelli, M. A., Minghini, M., Molinari, M. E., & Zamboni, G. (2016). Positional accuracy assessment of the OpenStreetMap buildings layer through automatic homologous pairs detection: The method and a case study.‏
5- Da Costa, J. N. (2016). Novel tool for examination of data completeness based on a comparative study of VGI data and official building datasets. Geodetski vestnik, 60, 495-508.‏
6- Fan, H., Zipf, A., Fu, Q., & Neis, P. (2014). “Quality assessment for building footprints data on OpenStreetMap,” International Journal of Geographical Information Science, vol. 28, pp. 700-719.
7- Fram, C., Chistopoulou, K., & Ellul, C. (2015). “Assessing the quality of OpenStreetMap building data and searching for a proxy variable to estimate OSM building data completeness,” In: Proceedings of the 23rd GIS Research UK (GISRUK) conference, Leeds, UK, 17-15.
8- Geiß et al. (2011). “Remote Sensing-Based Characterization of Settlement Structures for Assessing Local Potential of District Heat,” Remote Sens., pp. 1447-1471.
9- Goetz, M., & Zipf, A. (2012). “OpenStreetMap in 3D – Detailed Insights on the Current Situation in Germany, “In: Proceedings of the 15th AGILE International Conference on Geographic Information Science.
10- Haklay, M. (2010). “How good is volunteered geographical information? A comparative study of OpenStreetMap and Ordnance Survey datasets,” Environment and planning B: Planning and design, vol. 37, pp. 703 -682.
11- Hecht, R., Kunze, C., & Hahmann, S. (2013). “Measuring completeness of building footprints in OpenStreetMap over space and time”, ISPRS International Journal of Geo-Information, vol. 2, pp.  1066-1091.‏
12- Huh, Y., Yu, K., & Heo, J. (2011). “Detecting conjugate-point pairs for map alignment between two polygon datasets,” Computers, Environment and Urban Systems, vol. 35, pp. 250-262.
13- Jilani, M., Bertolotto, M., Corcoran, P., & Alghanim, A. (2019). Traditional vs. Machine-Learning Techniques for OSM Quality Assessment. In Geospatial Intelligence: Concepts, Methodologies, Tools, and Applications,pp. 47-48.‏
14- Krek, A., Rumor, M., Zlatanova, S., & Fendel, E. M. (2009). “Interoperable Location Based Services for 3D cities on the Web using user generated content from OpenStreetMap”, in Urban and Regional Data Management: CRC Press, pp. 87-96.
15- Kresse, W., & Faddier, K. (2004). “ISO standards for geographic information.” Springer Science & Business Media.
16- Lehmler S, Murshed SM, Ansart L, Shen Y. (2019). Usability of open data for smart city applications–evaluation of data, development of application and creation of visual dashboards. REAL CORP. 2019 Apr 4.
17- LONG, T., & Jiao, W. (2012). “The geometric correction model based on areal features for multisource images.
18- Minaei, M. (2020). Evolution, density and completeness of OpenStreetMap road networks in developing countries: the case of Iran. Applied Geography, 119, 102246.‏
19- Over,M., Schilling, A., Neubauer, S., & Zipf, A.( 2010). “Generating web-based 3D City Models from OpenStreetMap: The current situation in Germany”, Computers, Environment and urban systems, vol. 34, pp. 496-507.
20- Schmitz, S., Zipf, A., & Neis, P. (2008) “New applications based on collaborative geodata—the case of routing”, in Proceedings of XXVIII INCA international congress on collaborative mapping and space technology.
21- Sehra, S. S., Singh, J., & Rai, H. S. (2017). Assessing OpenStreetMap data using intrinsic quality indicators: an extension to the QGIS processing toolbox. Future Internet, vol. 9, 15.‏
22- Senaratne, H., Mobasheri, A., Ali, A. L., Capineri, C., & Haklay, M. (2017). A review of volunteered geographic information quality assessment methods. International Journal of Geographical Information Science, 31, 139-167.‏
23- Tang, W., Hao, Y., Zhao, Y., & Li, N. (2008).”Feature matching algorithm based on spatial similarity,” In: Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Classification of Remote Sensing Images. International Society for Optics and Photonics.
24- Tornros, T., Dorn, H., Hahmann, S., & Zipf, A. (2015). “Uncertainties of completeness measures in OpenStreetMap–A case study for buildings in a medium-sized German city,” ISPRS annals of the photogrammetry, remote sensing and spatial information sciences, 2, 353.‏
25- Veregin, H. (1999). Data quality parameters.
Geographical information systems, 1, 177-189.‏
26- Wang, Z., & Zipf, A. (2017). Using OpenStreetMap Data To Generate Building Models With Their Inner Structures For 3D Maps. ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, 4.‏
27- Xu, Y., Chen, Z., Xie, Z., & Wu, L. (2017). “Quality assessment of building footprint data using a deep autoencoder network,” International Journal of Geographical Information Science, vol. 31, pp. 1929-1951.
28- Zhonglianga F., & Jianhuaa, W. (2008). “Entity matching in vector spatial data,” in Proceedings of the XXI ISPRS Congress, pp. 1467-147.
29- www.smartcity.press/data-benefits-in-smart-cities