A review of volunteered geographic information quality assessment methods

References

• Agichtein, E., et al., 2008. Finding high-quality content in social media. In: Proceedings of the 2008 international conference on web search and data mining, 11–12 February Palo Alto, CA. New York: ACM, 183–194.
   Google Scholar
• Al-Bakri, M. and Fairbairn, D., 2010. Assessing the accuracy of crowdsourced data and its integration with official spatial datasets. In: N.J. Tate and P.F. Fisher, eds. Proceedings of the ninth international symposium on spatial accuracy assessment in natural resources and environmental sciences, 20–23 July, Leicester. 317–320.
   Google Scholar
• Ali, A.L., et al., 2014. Ambiguity and plausibility: managing classification quality in volunteered geographic information. In: Proceedings of the 22nd international conference on geographic information systems.
   Google Scholar
• Andrienko, G., et al., 2009. Analysis of community-contributed space-and time-referenced data (example of Flickr and panoramio photos). In: IEEE symposium on visual analytics science and technology, 2009. VAST 2009, 12–13 October Atlantic City, NJ. Piscataway, NJ: IEEE, 213–214.
   Google Scholar
• Andrienko, G., et al., 2013. Thematic patterns in georeferenced tweets through space-time visual analytics. Computing in Science and Engineering, 15 (3), 72–82. doi:10.1109/MCSE.2013.70
   Web of Science ®Google Scholar
• Antoniou, V., Morley, J., and Haklay, M., 2010. Web 2.0 geotagged photos: assessing the spatial dimension of the phenomenon. Geomatica, 64 (1), 99–110.
   Google Scholar
• Antoniou, V. and Schlieder, C., 2014. Participation patterns, VGI and gamification. In: J. Huerta, S. Schade, C. Granell, eds. AGILE 2014. Cham: Springer International Publishing, 3–6.
   Google Scholar
• Antoniou, V. and Skopeliti, A., 2015. Measures and indicators of VGI quality: an overview. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 1, 345–351. doi:10.5194/isprsannals-II-3-W5-345-2015
   Google Scholar
• Arsanjani, J.J., et al., 2015. Quality assessment of the contributed land use information from OpenStreetMap versus authoritative datasets. In: J. Arsanjani, et al., eds. OpenStreetMap in GIScience. Cham: Springer, 37–58.
   Google Scholar
• Ather, A., 2009. A quality analysis of openstreetmap data. ME Thesis, University College London, London, UK.
   Google Scholar
• Barron, C., Neis, P., and Zipf, A., 2014. A comprehensive framework for intrinsic OpenStreetMap quality analysis. Transactions in GIS, 18 (6), 877–895.
   Web of Science ®Google Scholar
• Becker, H., Naaman, M., and Gravano, L., 2011. Selecting quality twitter content for events. In: ICWSM, 17–21 July Barcelona. Menlo Park, CA: AAAI, 11.
   Google Scholar
• Bishr, M. and Janowicz, K., 2010. Can we trust information? – The case of volunteered geographic information. In: A. Devaraju, et al., eds. Towards digital earth search discover and share geospatial data workshop at future internet symposium [online], 20 September Berlin. Vol. 640. Available from: CEUR-WS.org.
   Google Scholar
• Bordogna, G., et al., 2014. A linguistic decision making approach to assess the quality of volunteer geographic information for citizen science. Information Sciences, 258, 312–327. doi:10.1016/j.ins.2013.07.013
   Web of Science ®Google Scholar
• Bosch, H., et al., 2013. Scatterblogs2: real-time monitoring of microblog messages through user-guided filtering. IEEE Transactions on Visualization and Computer Graphics, 19 (12), 2022–2031. doi:10.1109/TVCG.2013.186
   PubMed Web of Science ®Google Scholar
• Brando, C. and Bucher, B., 2010. Quality in user generated spatial content: a matter of specifications. In: Proceedings of the 13th AGILE international conference on geographic information science, 11–14 May Guimarães. Cham: Springer International Publishing, 1–8.
   Google Scholar
• Bulearca, M. and Bulearca, S., 2010. Twitter: a viable marketing tool for SMEs. Global Business and Management Research: an International Journal, 2 (4), 296–309.
   Google Scholar
• Canavosio-Zuzelski, R., Agouris, P., and Doucette, P., 2013. A photogrammetric approach for assessing positional accuracy of OpenStreetMap© roads. ISPRS International Journal of Geo-Information, 2 (2), 276–301. doi:10.3390/ijgi2020276
   Web of Science ®Google Scholar
• Canini, K.R., Suh, B., and Pirolli, P.L., 2011. Finding credible information sources in social networks based on content and social structure. In: IEEE Third International Conference on Privacy, Security, Risk and Trust (PASSAT) and 2011 IEEE third inernational conference on social computing (SocialCom), 2011, 9–11 October Boston, MA. Piscataway, NJ: IEEE, 1–8.
   Google Scholar
• Castillo, C., Mendoza, M., and Poblete, B., 2011. Information credibility on twitter. In: Proceedings of the 20th international conference on World Wide Web, 28 March–1 April Hyderabad, Andhra Pradesh. New York: ACM, 675–684.
   Google Scholar
• Chunara, R., Andrews, J.R., and Brownstein, J.S., 2012. Social and news media enable estimation of epidemiological patterns early in the 2010 Haitian cholera outbreak. The American Journal of Tropical Medicine and Hygiene, 86 (1), 39–45. doi:10.4269/ajtmh.2012.11-0597
   PubMed Web of Science ®Google Scholar
• Ciepluch, B., et al., 2010. Comparison of the accuracy of OpenStreetMap for Ireland with Google Maps and Bing Maps. In: N.J. Tate and P.F. Fisher, eds. Proceedings of the ninth international symposium on spatial accuracy assessment in natural resuorces and enviromental sciences 20–23 July Leicester, 337.
   Google Scholar
• Ciepluch, B., et al., 2011. Assessing the quality of open spatial data for mobile location-based services research and applications. In: Polish Society for Photogrammetry and Remote Sensing, editor. Archives of photogrammetry, cartography and remote sensing, Vol. 22. 105-116, ISSN 2083-2214.
   Google Scholar
• Codescu, M., et al., 2011. Osmonto-an ontology of openstreetmap tags. In: Conference proceedings of the the state of the map Europe (SOTM-EU), 15–17 July Vienna.
   Google Scholar
• Corcoran, P., Mooney, P., and Winstanley, A., 2010. Topological consistent generalization of OpenStreetMap [online]. Available from: http://bit.ly/1U2OeyV
   Google Scholar
• Craglia, M., Ostermann, F., and Spinsanti, L., 2012. Digital Earth from vision to practice: making sense of citizen-generated content. International Journal of Digital Earth, 5 (5), 398–416. doi:10.1080/17538947.2012.712273
   Web of Science ®Google Scholar
• Da Silva, A.C. and Wu, S.T., 2007. Consistent handling of linear features in polyline simplification. In: C.A. Davis Jr. and A.M.V. Monteiro, eds. Advances in geoinformatics. Cham: Springer, 1–17.
   Google Scholar
• De Longueville, B., Ostländer, N., and Keskitalo, C., 2010. Addressing vagueness in Volunteered Geographic Information (VGI)–A case study. International Journal of Spatial Data Infrastructures Research, 5, 1725–0463.
   Google Scholar
• De Tré, G., et al., 2010. Consistently handling geographical user data. In: E. Hüllermeier, R. Kruse, and F. Hoffmann, eds. Information processing and management of uncertainty in knowledge-based systems, applications, 28 June–2 July Dortmund. Berlin: Springer, 85–94.
   Google Scholar
• Fan, H., et al., 2014. Quality assessment for building footprints data on OpenStreetMap. International Journal of Geographical Information Science, 28 (4), 700–719. doi:10.1080/13658816.2013.867495
   Web of Science ®Google Scholar
• Flanagin, A.J. and Metzger, M.J., 2008. The credibility of volunteered geographic information. GeoJournal, 72 (3–4), 137–148. doi:10.1007/s10708-008-9188-y
   Google Scholar
• Foody, G., et al., 2014. Accurate attribute mapping from volunteered geographic information: issues of volunteer quantity and quality. The Cartographic Journal, 52 (4). doi:10.1179/1743277413Y.0000000070.
   Web of Science ®Google Scholar
• Forghani, M. and Delavar, M.R., 2014. A quality study of the OpenStreetMap dataset for Tehran. ISPRS International Journal of Geo-Information, 3 (2), 750–763. doi:10.3390/ijgi3020750
   Web of Science ®Google Scholar
• Frew, J., 2007. Provenance and volunteered geographic information [online]. Available from: http://www.ncgia.ucsb.edu/projects/vgi/docs/position/Frew_paper.pdf [Accessed 10 March 2008].
   Google Scholar
• Fuchs, G., et al., 2013. Tracing the German centennial flood in the stream of tweets: first lessons learned. In: D. Pfoser and A. Voisard, eds. Proceedings of the second ACM SIGSPATIAL international workshop on crowdsourced and volunteered geographic information, 5–8 November Orlando, FL. New York: ACM, 31–38.
   Google Scholar
• Girres, J.-F. and Touya, G., 2010. Quality assessment of the French OpenStreetMap dataset. Transactions in GIS, 14 (4), 435–459. doi:10.1111/tgis.2010.14.issue-4
   Web of Science ®Google Scholar
• Golder, S.A. and Huberman, B.A., 2006. Usage patterns of collaborative tagging systems. Journal of Information Science, 32 (2), 198–208. doi:10.1177/0165551506062337
   Web of Science ®Google Scholar
• Goodchild, M.F., 2007. Citizens as sensors: the world of volunteered geography. GeoJournal, 69 (4), 211–221. doi:10.1007/s10708-007-9111-y
   Google Scholar
• Goodchild, M.F. and Li, L., 2012. Assuring the quality of volunteered geographic information. Spatial Statistics, 1, 110–120. doi:10.1016/j.spasta.2012.03.002
   Web of Science ®Google Scholar
• Guinée, J.B., 2002. Handbook on life cycle assessment operational guide to the ISO standards. The International Journal of Life Cycle Assessment, 7 (5), 311–313. doi:10.1007/BF02978897
   Google Scholar
• Gupta, A. and Kumaraguru, P., 2012. Credibility ranking of tweets during high impact events. In: Proceedings of the 1st workshop on privacy and security in online social media, 17 April Lyon. New York: ACM, 2.
   Google Scholar
• Haklay, M., 2010. How good is volunteered geographic information? A comparative study of OpenStreetMap and Ordnance survey datasets. Environment and Planning. B, Planning & Design, 37 (4), 682. doi:10.1068/b35097
   Web of Science ®Google Scholar
• Haklay, M., et al., 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 (4), 315–322.
   Google Scholar
• Hartig, O., 2009. Provenance information in the web of data. In: C. Bizer, et al., eds. LDOW [online], 20 April Madrid, 538. Available from: CEUR-WS.org
   Google Scholar
• Hasan Dalip, D., et al., 2009. Automatic quality assessment of content created collaboratively by web communities: a case study of wikipedia. In: Proceedings of the 9th ACM/IEEE-CS joint conference on digital libraries, 14–19 June Austin, TX. New York: ACM, 295–304.
   Google Scholar
• Hashemi, P. and Abbaspour, R.A., 2015. Assessment of logical consistency in OpenStreetMap based on the spatial similarity concept. In: J. Arsanjani, et al., eds. OpenStreetMap in GIScience. Cham: Springer International Publishing, 19–36.
   Google Scholar
• Hecht, R., Kunze, C., and Hahmann, S., 2013. Measuring completeness of building footprints in OpenStreetMap over space and time. ISPRS International Journal of Geo-Information, 2 (4), 1066–1091. doi:10.3390/ijgi2041066
   Web of Science ®Google Scholar
• Helbich, M., et al., 2012. Comparative spatial analysis of positional accuracy of OpenStreetMap and proprietary geodata. In: T. Jekel, et al., eds. Proceedings of GI_Forum 2012, 3–6 July Berlin. Berlin: Wichmann & ÖAW-Verlag, 24–33.
   Google Scholar
• Hollenstein, L. and Purves, R., 2014. Exploring place through user-generated content: using Flickr tags to describe city cores. Journal of Spatial Information Science, 1, 21–48.
   Google Scholar
• Hovland, C.I., Janis, I.L., and Kelley, H.H., 1953. Communication and persuasion; psychological studies of opinion change. New Haven: Yale University Press.
   Google Scholar
• Hoyle, D., 2001. ISO 9000: quality systems handbook. Oxford: Butterworth and Heinemann.
   Google Scholar
• Huang, K.L., Kanhere, S.S., and Hu, W., 2010. Are you contributing trustworthy data?: the case for a reputation system in participatory sensing. In: Proceedings of the 13th ACM international conference on modeling, analysis, and simulation of wireless and mobile systems, 17–21 October Bodrum. New York: ACM, 14–22.
   Google Scholar
• Huberman, B.A., Romero, D.M., and Wu, F., 2008. Social networks that matter: twitter under the microscope. arXiv preprint arXiv:0812.1045. Chicago, IL: University of Illinois.
   Google Scholar
• Jackson, S.P., et al., 2013. Assessing completeness and spatial error of features in volunteered geographic information. ISPRS International Journal of Geo-Information, 2 (2), 507–530. doi:10.3390/ijgi2020507
   Web of Science ®Google Scholar
• Jacob, R., et al., 2009. Campus guidance system for international conferences based on openstreetmap. In: J.M. Ware and G.E. Taylor, eds. Web and wireless geographical information systems. Berlin: Springer, 187–198.
   Google Scholar
• Jacobs, N., et al., 2007. Geolocating static cameras. In: IEEE 11th international conference on Computer vision, 2007. ICCV 2007, 14–20 October Rio de Janeiro. Piscataway, NJ: IEEE, 1–6.
   Google Scholar
• Kang, B., O’Donovan, J., and Höllerer, T., 2012. Modeling topic specific credibility on twitter. In: Proceedings of the 2012 ACM international conference on intelligent user interfaces, 14–17 February Lisbon. New York: ACM, 179–188.
   Google Scholar
• Keßler, C., et al., 2009. Bottom-up gazetteers: learning from the implicit semantics of geotags. In: K. Janowicz, M. Raubal, and S. Levashkin, eds. GeoSpatial semantics. Berlin: Springer, 83–102.
   Google Scholar
• Keßler, C. and de Groot, R.T.A., 2013. Trust as a proxy measure for the quality of volunteered geographic information in the case of OpenStreetMap. In: D. Vandenbroucke, B. Bucher, and J. Crompvoets, eds. Geographic information science at the heart of Europe. Cham: Springer, 21–37.
   Google Scholar
• Keßler, C., Trame, J., and Kauppinen, T., 2011. Tracking editing processes in volunteered geographic information: the case of OpenStreetMap. In: M. Duckham, A. Galton, and M. Worboys, eds. Identifying objects, processes and events in spatio-temporally distributed data (IOPE), workshop at conference on spatial information theory 12–16 September Belfast, ME. Vol. 12.
   Google Scholar
• Koukoletsos, T., Haklay, M., and Ellul, C., 2012. Assessing data completeness of VGI through an automated matching procedure for linear data. Transactions in GIS, 16 (4), 477–498. doi:10.1111/j.1467-9671.2012.01304.x
   Web of Science ®Google Scholar
• Kounadi, O., 2009. Assessing the quality of OpenStreetMap data. Thesis (MSc). University College of London.
   Google Scholar
• Lindeberg, T., 2012. Scale invariant feature transform. Scholarpedia, 7 (5), 10491. doi:10.4249/scholarpedia.10491
   Google Scholar
• Liu, S.B., et al., 2008. F. Fiedrich and B. Van de Walle, eds. In search of the bigger picture: the emergent role of on-line photo sharing in times of disaster. In: Proceedings of the Information Systems for Crisis Response and Management conference (ISCRAM), 4–7 May Washington, DC.
   Google Scholar
• MacEachren, A.M., et al., 2011. Senseplace2: geotwitter analytics support for situational awareness. In: IEEE conference on Visual Analytics Science and Technology (VAST), 2011, 23–28 October Providence, RI. Piscataway, NJ: IEEE, 181–190.
   Google Scholar
• Maué, P., 2007. Reputation as tool to ensure validity of VGI. In: VGI specialist meeting, 13–14 December Santa Barbara, CA.
   Google Scholar
• McDougall, K., 2009. The potential of citizen volunteered spatial information for building SDI. In: GSDI 11 world conference: spatial data infrastructure convergence: building SDI bridges to address global challenges. 15–19 June Rotterdam. GSDI Association Press.
   Google Scholar
• Mendoza, M., Poblete, B., and Castillo, C., 2010. Twitter under crisis: can we trust what we RT? In: Proceedings of the first workshop on social media analytics, 25–28 July Washington, DC. New York: ACM, 71–79.
   Google Scholar
• Milholland, N. and Pultar, E., 2013. The San Francisco public art map application: using VGI and social media to complement institutional data sources. In: Proceedings of the 1st ACM SIGSPATIAL international workshop on MapInteraction, 5–8 November Orlando, FL. New York: ACM, 48–53.
   Google Scholar
• Montello, D.R., et al., 2003. Where’s downtown? Behavioral methods for determining referents of vague spatial queries. Spatial Cognition & Computation, 3 (2–3), 185–204. doi:10.1080/13875868.2003.9683761
   Google Scholar
• Mooney, P. and Corcoran, P., 2012. The annotation process in OpenStreetMap. Transactions in GIS, 16 (4), 561–579. doi:10.1111/j.1467-9671.2012.01306.x
   Web of Science ®Google Scholar
• Morris, M.R., et al., 2012. Tweeting is believing? Understanding microblog credibility perceptions. In: Proceedings of the ACM 2012 conference on computer supported cooperative work, 11–15 February Seattle, WA. New York: ACM, 441–450.
   Google Scholar
• Mullen, W.F., et al., 2014. Assessing the impact of demographic characteristics on spatial error in volunteered geographic information features. GeoJournal, 80 (4), 587–605.
   Web of Science ®Google Scholar
• Mummidi, L.N. and Krumm, J., 2008. Discovering points of interest from users’ map annotations. GeoJournal, 72 (3–4), 215–227. doi:10.1007/s10708-008-9181-5
   Google Scholar
• Neis, P., Zielstra, D., and Zipf, A., 2011. The street network evolution of crowdsourced maps: OpenStreetMap in Germany 2007–2011. Future Internet, 4 (4), 1–21. doi:10.3390/fi4010001
   Google Scholar
• Neis, P., Zielstra, D., and Zipf, A., 2013. Comparison of volunteered geographic information data contributions and community development for selected world regions. Future Internet, 5 (2), 282–300. doi:10.3390/fi5020282
   Google Scholar
• O’Connor, R., 2009. GLOBAL: Facebook and Twitter ’reshaping journalism as we know it’ [online]. Available from: http://bit.ly/1InIn6V [Accessed 22 December 2015].
   Google Scholar
• O’Donovan, J., et al., 2012. Credibility in context: an analysis of feature distributions in twitter. In: Privacy, Security, Risk and Trust (PASSAT), 2012 international conference on and 2012 international confernece on social computing (SocialCom), 3–5 September Amsterdam. Piscataway, NJ: IEEE, 293–301.
   Google Scholar
• Ostermann, F.O. and Spinsanti, L., 2011. A conceptual workflow for automatically assessing the quality of volunteered geographic information for crisis management. In: S. Geertman, W. Reinhardt, and F. Toppen, eds. Proceedings of AGILE, 18–21 April Utrecht. Berlin: Springer.
   Google Scholar
• Popescu, A., Grefenstette, G., and Moëllic, P.A., 2008. Gazetiki: automatic creation of a geographical gazetteer. In: Proceedings of the 8th ACM/IEEE-CS joint conference on digital libraries, 16–20 June Pittsburgh, PA. New York: ACM, 85–93.
   Google Scholar
• Poser, K. and Dransch, D., 2010. Volunteered geographic information for disaster management with application to rapid flood damage estimation. Geomatica, 64 (1), 89–98.
   Google Scholar
• Resnick, P., et al., 2000. Reputation systems. Communications of the ACM, 43 (12), 45–48. doi:10.1145/355112.355122
   Web of Science ®Google Scholar
• Robinson, S., et al., 2012. Navigation your way: from spontaneous independent exploration to dynamic social journeys. Personal and Ubiquitous Computing, 16 (8), 973–985. doi:10.1007/s00779-011-0457-2
   Web of Science ®Google Scholar
• Sacha, D., et al., 2016. The role of uncertainty, awareness, and trust in visual analytics. IEEE Transactions on Visualization and Computer Graphics, 22 (1), 240–249. doi:10.1109/TVCG.2015.2467591
   PubMed Web of Science ®Google Scholar
• Sakaki, T., Okazaki, M., and Matsuo, Y., 2010. Earthquake shakes Twitter users: real-time event detection by social sensors. In: Proceedings of the 19th international conference on World Wide Web, 26–30 April Raleigh, NC. New York: ACM, 851–860.
   Google Scholar
• Schmitz, S., Neis, P., and Zipf, A., 2008. New applications based on collaborative geodata—the case of routing. In: Proceedings of XXVIII INCA international congress on collaborative mapping and space technology, 4–6 November Gandhinagar.
   Google Scholar
• Senaratne, H., et al., 2014. Moving on Twitter: using episodic hotspot and drift analysis to detect and characterise spatial trajectories. In: 7th ACM SIGSPATIAL International Workshop on Location-Based Social Networks (LBSN 2014), 4–7 November Dallas, TX. New York: ACM.
   Google Scholar
• Senaratne, H., Bröring, A., and Schreck, T., 2013. Using reverse viewshed analysis to assess the location correctness of visually generated VGI. Transactions in GIS, 17 (3), 369–386. doi:10.1111/tgis.12039
   Web of Science ®Google Scholar
• Siebritz, L.A., 2014. Assessing the accuracy of openstreetmap data in South Africa for the purpose of integrating it with authoritative data. Master’s thesis. University of Cape Town.
   Google Scholar
• Tenney, M., 2014. Quality evaluations on Canadian OpenStreetMap data. In: Spatial knowledge and information. Montreal, QC: McGill University.
   Google Scholar
• Thomson, J., et al., 2005. A typology for visualizing uncertainty. In: Electronic imaging 2005, 16–20 January San Jose, CA. International Society for Optics and Photonics, 146–157.
   Google Scholar
• Valli, C. and Hannay, P., 2010. Geotagging where cyberspace comes to your place. In: Proceedings of the international conference on security and management (SAM 10), 12–15 July Las Vegas, NV. Athens, GA. CSREA press, 627–632.
   Google Scholar
• Van Exel, M., Dias, E., and Fruijtier, S., 2010. The impact of crowdsourcing on spatial data quality indicators. In: S.I. Fabrikant, et al., eds. Proceedings of GiScience, 14–17 September Zurich. Berlin: Springer.
   Google Scholar
• Van Oort, P. and Bregt, A., 2005. Do users ignore spatial data quality? A decision-theoretic perspective. Risk Analysis, 25 (6), 1599–1610. doi:10.1111/risk.2005.25.issue-6
   PubMed Web of Science ®Google Scholar
• Vandecasteele, A. and Devillers, R., 2013. Improving volunteered geographic data quality using semantic similarity measurements. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 1 (1), 143–148. doi:10.5194/isprsarchives-XL-2-W1-143-2013
   Google Scholar
• Vandecasteele, A. and Devillers, R., 2015. Improving volunteered geographic information quality using a tag recommender system: the case of OpenStreetMap. In: J.J. Arsanjani, et al., eds. OpenStreetMap in GIScience. Cham: Springer, 59–80.
   Google Scholar
• Wang, D., et al., 2014. Using semantic technology for consistency checking of road signs. In: Z. Huang, et al., eds. Web Information Systems Engineering–WISE 2013 workshops, 13–15 October Nanjing. Berlin: Springer, 11–22.
   Google Scholar
• Will, J., 2014. Development of an automated matching algorithm to assess the quality of the OpenStreetMap road network: a case study in Göteborg, Sweden. Student thesis series INES. Lund: Lund University Publications.
   Google Scholar
• Yanenko, O. and Schlieder, C., 2014. Game principles for enhancing the quality of user-generated data collections. In: Proc. AGILE, workshop geogames geoplay, 3–16 June Castellon, 1–5.
   Google Scholar
• Zandbergen, P.A., Ignizio, D.A., and Lenzer, K.E., 2011. Positional accuracy of TIGER 2000 and 2009 road networks. Transactions in GIS, 15 (4), 495–519. doi:10.1111/tgis.2011.15.issue-4
   Web of Science ®Google Scholar
• Zhang, W. and Kosecka, J., 2006. Image based localization in urban environments. In: Third international symposium on 3D data processing, visualization, and transmission, 14–16 June Chapel Hill, NC. Piscataway, NJ: IEEE, 33–40.
   Google Scholar
• Zielstra, D. and Hochmair, H.H., 2013. Positional accuracy analysis of Flickr and Panoramio images for selected world regions. Journal of Spatial Science, 58 (2), 251–273. doi:10.1080/14498596.2013.801331
   Web of Science ®Google Scholar