References
- Basaraner M., Cetinkaya S., 2017, Performance of shape indices and classification schemes for characterising perceptual shape complexity of building footprints in GIS. “Intern. Journal of Geogr. Inform. Science” Vol. 31, No. 10, pp. 1952−1977.
- Basiri A., Haklay M., Foody G., Mooney P., 2019, Crowdsourced geospatial data quality: challenges and future directions. “Intern. Journal of Geogr. Inform. Science” Vol. 33, No. 8, pp. 1588−1593.
- Basiri A., Jackson M. Amirian P. Pourabdollah A., Sester M., Winstanley A., Moore T., Zhang L., 2016, Quality assessment of OpenStreetMap data using trajectory mining. “Geo-spatial Inform. Science” Vol. 19, No. 1, pp. 56−68.
- Brovelli M.A., Zamboni G.A, 2018, New method for the assessment of spatial accuracy and completeness of OpenStreetMap building footprints. “ISPRS Intern. Journal of Geoinformation” Vol. 7, No. 8, p. 289.
- Cartwright W., 2012, Neocartography: opportunities, issues and prospects. “South African Journal of Geomatics” Vol. 1, No.1, pp. 14−31.
- Erden O.E., Basaraner M., 2019, Geometric quality analysis of building footprints from OpenStreet-Map data in comparison to topographic map data. In: International Symposium on Advanced Engineering Technologies (ISADET), 2−4 May 2019, Kahramanmaras, Turkey.
- Fan H., Zipf A., Fu Q., Neis P., 2014, Quality assessment for building footprints data on OpenStreetMap. “Intern. Journal of Geogr. Inform. Science” Vol. 28, No. 4, pp. 700−719.
- Fonte C.C., Antoniou V., Bastin L., Estima J., Arsanjani J.J., Bayas J.-C.L., See L., Vatseva R., 2017, Assessing VGI data quality. In: G. Foody, L. See, S. Fritz, P. Mooney, A.-M. Olteanu-Raimond, C.C. Fonte, V. Antoniou (eds.), Mapping and the citizen sensor, pp. 137−163. London: Ubiquity Press.
- Hecht R., Kunze C., Hahmann S., 2013, Measuring completeness of building footprints in OpenStreet-Map over space and time. “ISPRS Intern. Journal of Geoinformation” Vol. 2, No. 4, pp. 1066−1091.
- Jacobs K.T., Mitchell S.W., 2020, OpenStreetMap quality assessment using unsupervised machine learning methods. “Trans GIS” Vol. 24, No. 5, pp. 1280−1298.
- Kohlstock P., 2014, Kartographie − eine Einführung, 3. Auflage. Paderborn: Schöningh (UTB).
- Kresse W., Fadaie K., 2004, ISO standards for geographic information. Berlin: Springer.
- Li Z., 2007, Algorithmic foundation of multi-scale spatial representation. Boca Raton: CRC Press.
- Maidaneh Abdi I., Le Guilcher A., Olteanu-Raimond A.-M., 2020, A regression model of spatial accuracy prediction for OpenStreetMap buildings. “ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Inform. Sciences” Vol. V-4-2020, pp. 39−47.
- Nowak Da Costa J., 2016, Novel tool for examination of data completeness based on a comparative study of VGI data and official building datasets. “Geodetski Vestnik” Vol. 60, No. 3, pp. 495−508.
- See L., Estima J., Pődör A., Arsanjani J.J., Bayas J.-C.L.,Vatseva R., 2017, Sources of VGI for mapping. In: G. Foody, L. See, S. Fritz, P. Mooney, A.-M. Olteanu-Raimond., C.C. Fonte, V. Antoniou (eds.), Mapping and the citizen sensor, pp. 13−35. London: Ubiquity Press.
- Senaratne H., Mobasheri A., Ali A.L., Capineri C., Haklay M., 2017, A review of volunteered geographic information quality assessment methods. “Intern. Journal of Geogr. Inform. Science” Vol. 31, No. 1, pp. 139−167.
- Sui D., Goodchild M., Elwood S., 2013, Volunteered geographic information, the exaflood, and the growing digital divide. In: D. Sui, M. Goodchild, S. Elwood (eds.), Crowdsourcing geographic knowledge − volunteered geographic information (VGI) in theory and practice, pp. 1−12. New York: Springer.
- Xu Y., Chen Z., Xie Z., Wu L., 2017, Quality assessment of building footprint data using a deep auto-encoder network. “Intern. Journal of Geogr. Inform. Science” Vol. 31, No. 10, pp. 1929−1951.
- download.geofabrik.de/europe/turkey.html – Geofabrik downloads – Europe – Turkey (access 05.01.2020)
- iso.org/standard/32575.html – ISO 19157:2013 Geographic information – Data quality (access 03.12.2020)