Abstract
In the last decade, volunteers have been contributing massively to what we know nowadays as Volunteered Geographic Information (VGI). Through the research that has been conducted recently, it has become clear that this huge amount of information might hide interesting and rich geographical information. The OpenStreetMap (OSM) project is one of the most well-known and studied VGI initiatives. It has been studied to identify its potential for different applications. In the field of Land Use/Cover, an earlier study by the authors explored the use of OSM for Land Use/Cover (LULC) validation. Using the COoRdination of INformation on the Environment (CORINE) Land Cover (CLC) database as the Land Use reference data, they analyzed the OSM coverage and classification accuracy, finding an interesting global accuracy value of 76.7 % for level 1 land classes, for the study area of continental Portugal, despite a very small coverage value of approximately 3.27 %. In this chapter we review the existing literature on using OSM data for LULC database production and move this research forwards by exploring the suitability of the OSM Points of Interest dataset. We conclude that OSM can give very interesting contributions and that the OSM Points of Interest dataset is suitable for those classified as CLC class 1 which represents artificial surfaces.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Barron C, Neis P, Zipf A (2014) A comprehensive framework for intrinsic OpenStreetMap quality analysis. Trans GIS. doi:10.1111/tgis.12073
Baulies X, Szejwach G (1997) Survey of needs, gaps and priorities on data for land use/land cover change research. Report presented at LUCC data requirements workshop. Barcelona, Spain, 11–14 Nov 1997
Caetano M, Mata F, Freire S (2006) Accuracy assessment of the Portuguese CORINE Land Cover map. Glob Dev Environ Earth Obs Space:459–467
Cihlar J (2000) Land cover mapping of large areas from satellites: status and research priorities. Int J Remote Sens 21(6–7):1093–1114. doi:10.1080/014311600210092
Clark ML, Aide TM (2011) Virtual interpretation of earth web-interface tool (VIEW-IT) for collecting land-use/land-cover reference data. Remote Sens 3(3):601–620. doi:10.3390/rs3030601
Corine Land Cover Nomenclature (2011) Corine land cover nomenclature illustrated guide. In: Joint meeting Geoland2—EAGLE. Málaga, Spain, 23–24 June. http://sia.eionet.europa.eu/EAGLE/EAGLE_6thMeeting_g2_Malaga/04d_Nomenclature_CLC.pdf. Accessed 5 Jun 2014
Ellis E (2013) Land-use and land-cover change. In: The encyclopedia of earth. http://www.eoearth.org/view/article/51cbee4f7896bb431f696e92. Accessed 10 May 2014
Elwood S, Goodchild MF, Sui DZ (2012) Researching volunteered geographic information: spatial data, geographic research, and new social practice. Ann Assoc Am Geogr 102(3):571–590. doi:10.1080/00045608.2011.595657
Estima J, Painho M (2013a) 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 Press, pp 39–46. doi:10.1145/2534732.2534734
Estima J, Painho M (2013b) Flickr geotagged and publicly available photos: preliminary study of its adequacy for helping quality control of corine land cover. In: Murgante B, Misra S, Carlini M, Torre CM, Nguyen HQ, Taniar D, Gervasi O (eds) ICCSA 2013: computational science and its applications. The 13th international conference on computational science and its Applications, Ho Chi Minh City, Vietnam, 24-27 June 2013 . Lecture notes in computer science, vol 7974. Springer, Heidelberg, pp 205–220. doi:10.1007/978-3-642-39649-6_15
Estima J, Painho M (2014) photo based volunteered geographic information initiatives: a comparative study of their suitability for helping quality control of corine land cover. Int J Agric Environ Inf Syst 5(3):75–92. doi:10.4018/ijaeis.2014070105
European Environment Agency (2014) http://www.eea.europa.eu/data-and-maps/data/clc-2006-vector-data-version-2. Accessed 5 Jun 2014
Fan H, Zipf A, Fu Q, Neis P (2014) Quality assessment for building footprints data on OpenStreetMap. Int J Geogr Inf Sci 28(4):700–719. doi:10.1080/13658816.2013.867495
Flickr (2014) https://www.flickr.com/. Accessed 5 Jun 2014
Foody GM, Boyd DS (2013) Using volunteered data in land cover map validation: mapping West African forests. IEEE J Sel Top Appl Earth Obs Remote Sens 6(3):1305–1312. doi:10.1109/JSTARS.2013.2250257
Fritz S, McCallum I, Schill C, Perger C, Grillmayer R, Achard F, Obersteiner M (2009) Geo-Wiki.Org: The use of crowdsourcing to improve global land cover. Remote Sens 1(3):345–354. doi:10.3390/rs1030345
Fritz S, McCallum I, Schill C, Perger C, See L, Schepaschenko D, Obersteiner M (2012) Geo-Wiki: an online platform for improving global land cover. Environ Model Softw 31:110–123. doi:10.1016/j.envsoft.2011.11.015
Geofabrik (2014) http://www.geofabrik.de/. Accessed 5 Jun 2014
GMapCreator (2014) http://www.bartlett.ucl.ac.uk/casa/latest/software/gmap_creator. Accessed 5 Jun 2014
Goodchild M (2007) Citizens as sensors: the world of volunteered geography. GeoJournal 69(4):211–221. doi:10.1007/s10708-007-9111-y
Goodchild M (2008a) Assertion and authority: the science of user-generated geographic content. In: Proceedings of the Colloquium for Andrew U. Frank’s 60th birthday. Department of Geoinformation and Cartography, Vienna, Austria
Goodchild M (2008b) Commentary: whither VGI? GeoJournal 72(3–4):239–244. doi:10.1007/s10708-008-9190-4
Goodchild M, Glennon JA (2010) Crowdsourcing geographic information for disaster response: a research frontier. Int J Digit Earth 3(3):231–241. doi:10.1080/17538941003759255
Google MyMaps (2014) https://www.google.com/maps/d/. Accessed 5 Jun 2014
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(6):963–982. doi:10.1080/13658816.2011.619501
Hecht R, Kunze C, Hahmann S (2013) Measuring completeness of building footprints in OpenStreetMap over space and time. ISPRS Int J Geo-Inf 2(4):1066–1091. doi:10.3390/ijgi2041066
Hollenstein L, Purves R (2010) Exploring place through user-generated content: using Flickr to describe city cores. J Spat Inf Sci 1(1):21–48. doi:10.5311/JOSIS.2010.1.3
Holone H, Misund G, Holmstedt H (2007) Users are doing it for themselves: pedestrian navigation with user generated content. In: International conference on next generation mobile applications, services and technologies. IEEE, pp 91–99. doi: 10.1109/NGMAST.2007.4343406
Hudson-Smith A, Batty M, Crooks A, Milton R (2009) Mapping for the masses: accessing web 2.0 through crowdsourcing. Soc Sci Comput Rev 27(4):524–538. doi:10.1177/0894439309332299
Jokar Arsanjani J, Helbich M, Bakillah M (2013a) Exploiting volunteered geographic information to ease land use mapping of an urban landscape. In: International archives of the photogrammetry, remote sensing and spatial information sciences. 29th Urban data management symposium, vol XL-4/W1. London, United Kingdom, 29–31 May 2013
Jokar Arsanjani JJ, Helbich M, Bakillah M, Hagenauer J, Zipf A (2013b) Toward mapping land-use patterns from volunteered geographic information. Int J Geogr Inf Sci 27(12):2264–2278. doi:10.1080/13658816.2013.800871
Jokar Arsanjani J, Vaz E (2015: in-press) An assessment of a collaborative mapping approach for exploring land use patterns for several European metropolises. Int J Appl Earth Obs Geoinf
Leung D, Newsam S (2010) Proximate sensing: Inferring what-is-where from georeferenced photo collections. In: Conference on computer vision and pattern recognition CVPR. IEEE, San Francisco, CA, pp 2955–2962, 13–18 June 2010. doi:10.1109/CVPR.2010.5540040
London Profiler (2014) http://128.40.111.250/casa/websites/profiler.asp. Accessed 5 Jun 2014
MapTube (2014) http://www.maptube.org/. Accessed 5 Jun 2014
Mooney P, Corcoran P, Winstanley A (2010) A study of data representation of natural features in OpenStreetMap. In Proceedings of the 6th GIScience international conference on geographic information science, vol 150. Zurich, Switzerland, 14–17 Sept 2010
OpenStreetMap Map Features (2014) http://wiki.openstreetmap.org/wiki/Map_Features. Accessed 5 Jun 2014
Perger C, Fritz S, See L, Schill C, Van Der Velde M, Mccallum I, Obersteiner M (2012) A campaign to collect volunteered geographic information on land cover and human impact. In GI Forum 2012: Geovizualisation, Society and Learning. pp 83–91
Turner AJ (2006) Introduction to neogeography. Sebastopol, CA
Wikimapia (2014) http://wikimapia.org/. Accessed 5 Jun 2014
Zook M, Graham M, Shelton T, Gorman S (2010) Volunteered geographic information and crowdsourcing disaster relief: a case study of the Haitian Earthquake. World Med Health Policy 2(2):6–32. doi:10.2202/1948-4682.1069
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Estima, J., Painho, M. (2015). Investigating the Potential of OpenStreetMap for Land Use/Land Cover Production: A Case Study for Continental Portugal. 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_14
Download citation
DOI: https://doi.org/10.1007/978-3-319-14280-7_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14279-1
Online ISBN: 978-3-319-14280-7
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)