Skip to main content

Quality Assessment of the Contributed Land Use Information from OpenStreetMap Versus Authoritative Datasets

Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)

Abstract

Land use (LU) maps are an important source of information in academia and for policy-makers describing the usage of land parcels. A large amount of effort and monetary resources are spent on mapping LU features over time and at local, regional, and global scales. Remote sensing images and signal processing techniques, as well as land surveying are the prime sources to map LU features. However, both data gathering approaches are financially expensive and time consuming. But recently, Web 2.0 technologies and the wide dissemination of GPS-enabled devices boosted public participation in collaborative mapping projects (CMPs). In this regard, the OpenStreetMap (OSM) project has been one of the most successful representatives, providing LU features. The main objective of this paper is to comparatively assess the accuracy of the contributed OSM-LU features in four German metropolitan areas versus the pan-European GMESUA dataset as a reference. Kappa index analysis along with per-class user’s and producers’ accuracies are used for accuracy assessment. The empirical findings suggest OSM as an alternative complementary source for extracting LU information whereas exceeding 50 % of the selected cities are mapped by mappers. Moreover, the results identify which land types preserve high/moderate/low accuracy across cities for urban LU mapping. The findings strength the potential of collaboratively collected LU features for providing temporal LU maps as well as updating/enriching existing inventories. Furthermore, such a collaborative approach can be used for collecting a global coverage of LU information specifically in countries in which temporal and monetary efforts could be minimized.

Keywords

  • Land use features
  • Comparative assessment
  • Global monitoring for environment and security urban atlas (GMESUA)
  • OpenStreetMap
  • Confusion matrix

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-14280-7_3
  • Chapter length: 22 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   119.00
Price excludes VAT (USA)
  • ISBN: 978-3-319-14280-7
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   159.99
Price excludes VAT (USA)
Hardcover Book
USD   179.99
Price excludes VAT (USA)
Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

References

  • Arino O, Ramos Perez JJ, Kalogirou V, Bontemps S, Defourny P, Van Bogaert E (2012) Global land cover map for 2009 (GlobCover 2009)

    Google Scholar 

  • Bakillah M, Lauer J, Liang SHL, Zipf A, Jokar Arsanjani J, Mobasheri A, Loos L (2014a) Exploiting big VGI to improve routing and navigation services. Big Data Tech Technol Geoinformatics 177–192

    Google Scholar 

  • Bakillah M, Liang S, Mobasheri A, Jokar Arsanjani J, Zipf A (2014b) Fine-resolution population mapping using OpenStreetMap points-of-interest. Int J Geogr Inf Sci 28: 1940–1963

    Google Scholar 

  • Birringer J (2008) Eye into Earth. Space Culture 11:59

    CrossRef  Google Scholar 

  • Büttner G, Feranec J, Gabriel J (2002) Corine land cover update 2000

    Google Scholar 

  • Castelein W, Grus Ł, Crompvoets J, Bregt AA (2010) Characterization of volunteered geographic information. In: 13th AGILE international conference on geographic information science 2010, Guimarães, pp 1–10

    Google Scholar 

  • Cihlar J, Jansen LJM (2001) From land cover to land use: a methodology for efficient land use mapping over large areas. Prof Geogr 53:275–289

    CrossRef  Google Scholar 

  • Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psychol Meas 20:37–46

    CrossRef  Google Scholar 

  • Comber A, See L, Fritz S, Van der Velde M, Perger C, Foody G (2013) Using control data to determine the reliability of volunteered geographic information about land cover. Int J Appl Earth Obs Geoinf 23:37–48

    CrossRef  Google Scholar 

  • Congalton RG (1991) A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens Environ 37:35–46

    CrossRef  Google Scholar 

  • De Leeuw J, Said M, Ortegah L, Nagda S, Georgiadou Y, DeBlois M (2011) An assessment of the accuracy of volunteered road map production in western Kenya. Remote Sens 3:247–256

    CrossRef  Google Scholar 

  • De Sherbinin A (2002) A CIESIN thematic guide to land land-use and land land-cover change (LUCC), NY, pp 10–20

    Google Scholar 

  • Devillers R, Bédard Y, Jeansoulin R, Moulin B (2007) Towards spatial data quality information analysis tools for experts assessing the fitness for use of spatial data. Int J Geogr Inf Sci 21:261–282

    CrossRef  Google Scholar 

  • Ellis E (2007) Land-use and land-cover change. Earth

    Google Scholar 

  • Estima J, Painho M (2013) Exploratory analysis of OpenStreetMap for land use classification. In: Proceedings of the second ACM SIGSPATIAL international workshop on crowdsourced and volunteered geographic information GEOCROWD’13, ACM, New York pp 39–46

    Google Scholar 

  • European Union (2011) Mapping guide for a european urban atlas

    Google Scholar 

  • Fan H, Zipf A, Fu Q, Neis P (2014) Quality assessment for building footprints data on OpenStreetMap. Int J Geogr Inf Sci 28:700–719

    CrossRef  Google Scholar 

  • Flanagin AJ, Metzger MJ (2008) The credibility of volunteered geographic information. GeoJournal 72:137–148

    CrossRef  Google Scholar 

  • Foody GM (2002) Status of land cover classification accuracy assessment. Remote Sens Environ 80:185–201

    CrossRef  Google Scholar 

  • Foody GM, See L, Fritz S, Van der Velde M, Perger C, Schill C, Boyd DS (2013) Assessing the accuracy of volunteered geographic information arising from multiple contributors to an internet based collaborative project. Trans GIS 17(6):847–860

    CrossRef  Google Scholar 

  • Fritz S, Bartholomé E, Belward A, Hartley A, Stibig HJ, Eva H, Mayaux P, Bartalev S, Latifovic R, Kolmert S et al (2003) Harmonisation, mosaicing and production of the global land cover 2000 database (beta version). Office for Official Publications of the European Communities Luxembourg, Luxembourg

    Google Scholar 

  • Fritz S, Mccallum I, Schill C, Perger C, See L, Schepaschenko D, van der Velde M, Kraxner F, Obersteiner M (2012) Geo-Wiki: an online platform for improving global land cover. Environ Model Softw 31:110–123

    CrossRef  Google Scholar 

  • Gervais M, Bédard Y, Levesque M, Bernier E, Devillers R (2009) Data quality issues and geographic knowledge discovery. Geogr Data Min Knowl Discov pp 99–115

    Google Scholar 

  • Goetz M, Zipf A (2010) Extending OpenStreetMap to indoor environments : bringing volunteered geographic information to the next level. CRC Press, Delft

    Google Scholar 

  • Goodchild MF (2007) Editorial: citizens as voluntary sensors: spatial data infrastructure in the world of web 2.0 2: 24–32

    Google Scholar 

  • Guptill SC, Morrison JL (1995) Elements of spatial data quality. Elsevier Science, Oxford

    Google Scholar 

  • Hagenauer J, Helbich M (2012) Mining urban land-use patterns from volunteered geographic information by means of genetic algorithms and artificial neural networks. Int J Geogr Inf Sci 26:963–982

    CrossRef  Google Scholar 

  • Haklay M (2010) How good is volunteered geographical information? A comparative study of OpenStreetMap and ordnance survey datasets. Environ Plan B Plan Des 37:682–703

    CrossRef  Google Scholar 

  • Hecht R, Kunze C, Hahmann S (2013) Measuring completeness of building footprints in OpenStreetMap over space and time. ISPRS Int J Geo-Information 2:1066–1091

    CrossRef  Google Scholar 

  • Helbich M, Amelunxen C, Neis P (2012) Comparative spatial analysis of positional accuracy of OpenStreetMap and proprietary Geodata. In: International GI_Forum, Salzburg

    Google Scholar 

  • Herold M, Mayaux P, Woodcock CE, Baccini A, Schmullius C (2008) Some challenges in global land cover mapping : an assessment of agreement and accuracy in existing 1 km datasets. Remote Sens Environ 112:2538–2556

    CrossRef  Google Scholar 

  • Hochmair HH, Zielstra D, Neis P (2014) Assessing the completeness of bicycle trail and lane features in OpenStreetMap for the United States. Trans GIS. doi:10.1111/tgis.12081

  • Jokar Arsanjani J, Helbich M, Bakillah M, Loos L (2015a) The emergence and evolution of OpenStreetMap: a cellular automata approach. Int J Digital Earth 8(1):74–88. http://www.tandfonline.com/doi/abs/10.1080/17538947.2013.847125

  • Jokar Arsanjani J, Vaz E (2015b) An assessment of a collaborative mapping approach for exploring land use patterns for several European metropolises. Int J Appl Earth Obs Geoinf 35:329–337

    CrossRef  Google Scholar 

  • Jokar Arsanjani J, Helbich M, Bakillah M, Hagenauer J, Zipf A (2013) Toward mapping land-use patterns from volunteered geographic information. Int J Geogr Inf Sci 27:2264–2278

    CrossRef  Google Scholar 

  • Jokar Arsanjani J, Mooney P, Helbich M, Zipf A (2015c) An exploration of future patterns of the contributions to OpenStreetMap and development of a Contribution Index, Trans GIS

    Google Scholar 

  • Jokar Arsanjani J, Vaz E, Bakillah M, Mooney P (2014) Towards initiating OpenLandMap founded on citizens’ science: the current status of land use features of OpenStreetMap in Europe. In: Huerta Schade G (ed) Proceedings of the AGILE’2014 international conference on geographic information science, 3–6 June 2014, AGILE digital editions, Castellón

    Google Scholar 

  • Kandrika S, Roy PSS (2008) Land use land cover classification of Orissa using multi-temporal IRS-P6 Awifs data: a decision tree approach. Int J Appl Earth Obs Geoinf 10:186–193

    CrossRef  Google Scholar 

  • Kasetkasem T, Arora MK, Varshney PK (2005) Super-resolution land cover mapping using a markov random field based approach. Remote Sens Environ 96:302–314

    CrossRef  Google Scholar 

  • Kong F, Yin H, Nakagoshi N, James P (2012) Simulating urban growth processes incorporating a potential model with spatial metrics. Ecol Indic 20:82–91

    CrossRef  Google Scholar 

  • Koukoletsos T, Haklay M, Ellul C (2012) Assessing data completeness of VGI through an automated matching procedure for linear data. Trans GIS 16(4):477–498

    CrossRef  Google Scholar 

  • Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 33:159–174

    CrossRef  Google Scholar 

  • Ludwig I, Voss A, Krause-Traudes M (2011) A comparison of the street networks of Navteq and OSM in Germany. In: Geertman S, Reinhardt W, Toppen F (eds) Advancing geoinformation science for a changing world SE-4 lecture notes in Geoinformation and cartography. Springer, Berlin, pp 65–84

    CrossRef  Google Scholar 

  • Mayaux P, Eva H, Gallego J, Strahler AH, Herold M, Member S, Agrawal S, Naumov S, De Miranda EE, Di Bella CM et al (2006) Validation of the global land cover 2000 map. IEEE Trans Geosci Remote Sens 44:1728–1739

    CrossRef  Google Scholar 

  • McIver D, Friedl M (2002) Using prior probabilities in decision-tree classification of remotely sensed data. Remote Sens Environ 81:253–261

    CrossRef  Google Scholar 

  • Mooney P, Corcoran P (2012) The Annotation Process in OpenStreetMap. Trans GIS 16:561–579

    Google Scholar 

  • Pacifici F, Chini M, Emery WJ (2009) A neural network approach using multi-scale textural metrics from very high-resolution panchromatic imagery for urban land-use classification. Remote Sens Environ 113:1276–1292

    CrossRef  Google Scholar 

  • Paneque-Gálvez J, Mas J-F, Moré G, Cristóbal J, Orta-Martínez M, Luz AC, Guèze M, Macía MJ, Reyes-García V (2013) Enhanced land use/cover classification of heterogeneous tropical landscapes using support vector machines and textural homogeneity. Int J Appl Earth Obs Geoinf 23:372–383

    CrossRef  Google Scholar 

  • Qi Z, Yeh AG-O, Li X, Lin Z (2012) A novel algorithm for land use and land cover classification using RADARSAT-2 polarimetric SAR data. Remote Sens Environ 118:21–39

    CrossRef  Google Scholar 

  • Ramm F (2014) OpenStreetMap data in layered GIS format http://www.geofabrik.de/data/geofabrik-osm-gis-standard-0.6.pdf

  • Ramm F, Names I, Files SS, Catalogue F, Features P, Features N, Related T, Infrastructure T, Generation P, Features L, et al (2011) OpenStreetMap data in layered GIS format pp 1–21

    Google Scholar 

  • Roick O, Hagenauer J, Zipf A (2011) OSMatrix—grid-based analysis and visualization of OpenStreetMap. In: State of the map EU 2011, Vienna, Austria

    Google Scholar 

  • Rouse LJ, Bergeron SJ, Harris TM (2007) Participating in the geospatial web: collaborative mapping, social networks and participatory GIS. In: Scharl A, Tochtermann K (eds) The geospatial web advanced information and knowledge processing. Springer, London, pp 153–158

    Google Scholar 

  • Saadat H, Adamowski J, Bonnell R, Sharifi F, Namdar M, Ale-Ebrahim S (2011) Land use and land cover classification over a large area in iran based on single date analysis of satellite imagery. ISPRS J Photogramm Remote Sens 66:608–619

    CrossRef  Google Scholar 

  • See L, Comber A, Salk C, Fritz S, van der Velde M, Perger C, Schill C, McCallum I, Kraxner F, Obersteiner M (2013) Comparing the quality of crowdsourced data contributed by expert and non-experts. PLoS ONE 8:e69958

    CrossRef  Google Scholar 

  • Seifert F (2009) Improving urban monitoring toward a European urban atlas. In: Global mapping of human settlement; remote sensing applications series. CRC Press, USA

    Google Scholar 

  • Sester M, Jokar Arsanjani J, Klammer R, Burghardt D, Haunert J-H (2014) Integrating and generalising volunteered geographic information. In: Burghardt D, Duchêne C, Mackaness W (eds) Abstracting geographic information in a data rich world, in series: lecture notes in geoinformation and cartography. Springer, Berlin, pp 119–155

    CrossRef  Google Scholar 

  • Sexton JO, Urban DL, Donohue MJ, Song C (2013) Long-term land cover dynamics by multi-temporal classification across the landsat-5 record. Remote Sens Environ 128:246–258

    CrossRef  Google Scholar 

  • Strahler AH, Boschetti L, Foody GM, Friedl MA, Hansen MC, Herold M, Mayaux P, Morisette JT, Stehman SV, Woodcock CE (2006) Global land cover validation: recommendations for evaluation and accuracy assessment of global land cover maps. Office for Official Publications of the European Communities, Luxemburg

    Google Scholar 

  • Thenkabail PS, Schull M, Turral H (2005) Ganges and Indus river basin land use/land cover (LULC) and irrigated area mapping using continuous streams of MODIS data. Remote Sens Environ 95:317–341

    CrossRef  Google Scholar 

  • Van Oort P (2006) Spatial data quality: from description to application. Wageningen University

    Google Scholar 

  • Vaz E, Nijkamp P, Painho M, Caetano M (2012) A multi-scenario forecast of urban change: a study on urban growth in the Algarve. Landsc Urban Plan 104:201–211

    CrossRef  Google Scholar 

  • Vaz E, Walczynska A, Nijkamp P (2013) Regional challenges in tourist wetland systems: an integrated approach to the Ria Formosa in the Algarve, Portugal. Reg Environ Change 13:33–42

    CrossRef  Google Scholar 

  • Wästfelt A, Arnberg W (2013) Local spatial context measurements used to explore the relationship between land cover and land use functions. Int J Appl Earth Obs Geoinf 23:234–244

    CrossRef  Google Scholar 

Download references

Acknowledgments

Jamal Jokar Arsanjani acknowledges the funding of the Alexander von Humboldt foundation.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jamal Jokar Arsanjani .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Jokar Arsanjani, J., Mooney, P., Zipf, A., Schauss, A. (2015). Quality Assessment of the Contributed Land Use Information from OpenStreetMap Versus Authoritative Datasets. In: Jokar Arsanjani, J., Zipf, A., Mooney, P., Helbich, M. (eds) OpenStreetMap in GIScience. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-319-14280-7_3

Download citation