AGILE 2015 pp 181-197 | Cite as

Towards a Qualitative Assessment of Changes in Geographic Vector Datasets

  • Karl RehrlEmail author
  • Richard Brunauer
  • Simon Gröchenig
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)


Changes are immanent to digital geographic vector datasets. While the majority of changes nowadays are quantitatively detectable by the use of geographic information systems their classification and impact assessment on a qualitative level with respect to specific data usage scenarios is often neglected. To close this gap, this work proposes a classification approach consisting of three parts: (1) a taxonomy for classifying quantitatively detectable edits in digital feature datasets (e.g. attribute or geometry changes), (2) a taxonomy for classifying edits into qualitative and therefore meaningful change types (e.g. feature revision or identity change) and (3) a mapping scheme providing the link from quantitative to qualitative classifications. In the context of a case study with OpenStreetMap history data the proposed classification approach is used to automatically detect and classify feature changes with respect to two feature types, namely streets and buildings, leading to a refined mapping scheme for two selected data usage scenarios, namely vehicle routing and map rendering. Results show the applicability of the approach, especially for assessing the impact of feature changes on different data usage scenarios, and provide a useful foundation for any change detection task in the context of geographic vector datasets.


Geographic vector data Change detection Qualitative assessment 



This work was partly funded by the Austrian Federal Ministry for Transport, Innovation and Technology.


  1. Abd El-Kawy, O. R., et al. (2011). Land use and land cover change detection in the western Nile delta of Egypt using remote sensing data. Applied Geography, 31(2), 483–494.CrossRefGoogle Scholar
  2. Blaschke, T., et al. (2014). Geographic object-based image analysis—towards a new paradigm. ISPRS Journal of Photogrammetry and Remote Sensing (official publication of the International Society for Photogrammetry and Remote Sensing (ISPRS)), 87(100), 180–191.CrossRefGoogle Scholar
  3. Chawathe, S. S., et al. (1996). Change detection in hierarchically structured information. ACM SIGMOD Record, 25(2), 493–504.CrossRefGoogle Scholar
  4. Chawathe, S. S., & Garcia-Molina, H. (1997). Meaningful change detection in structured data. ACM SIGMOD Record, 26(2), 26–37.CrossRefGoogle Scholar
  5. Chen, G., et al. (2012). Object-based change detection. International Journal of Remote Sensing, 33(14), 4434–4457.CrossRefGoogle Scholar
  6. Fonseca, F., et al. (2002). Semantic granularity in ontology-driven geographic information systems. AMAI Annals of Mathematics and Artificial Intelligence, 36(Special Issue on Spatial and Temporal Granularity), 121–151.CrossRefGoogle Scholar
  7. Frontiera, P., Larson, R., & Radke, J. (2008). A comparison of geometric approaches to assessing spatial similarity for GIR. International Journal of Geographical Information Science, 22(3), 337–360.CrossRefGoogle Scholar
  8. Goesseln, G., & Sester, M. (2005). Change detection and integration of topographic updates from ATKIS to geoscientific data sets. Next generation geospatial information (pp. 85–100).Google Scholar
  9. Gomes, J., & Velho, L. (1995). Abstraction paradigms for computer graphics. The Visual Computer, 11(5), 227–239.CrossRefGoogle Scholar
  10. Hussain, M., et al. (2013). Change detection from remotely sensed images: From pixel-based to object-based approaches. ISPRS Journal of Photogrammetry and Remote Sensing, 80, 91–106.CrossRefGoogle Scholar
  11. ISO. (2002). ISO 19101:2002 Geographic information—Reference model.Google Scholar
  12. Janowicz, K., Scheider, S., & Adams, B. (2013). A geo-semantics flyby. Reasoning web. Semantic technologies for intelligent data access. Lecture Notes in Computer Science (vol 8067, pp. 230–250).Google Scholar
  13. Klein, I., Gessner, U., & Kuenzer, C. (2012). Regional land cover mapping and change detection in Central Asia using MODIS time-series. Applied Geography, 35(1–2), 219–234.CrossRefGoogle Scholar
  14. Kottman, C., & Reed, C. (2009). The OpenGIS abstract specification, topic 5: Features.Google Scholar
  15. Mooney, P., & Corcoran, P. (2012). Characteristics of heavily edited objects in OpenStreetMap. Future Internet, 4(1), 285–305.CrossRefGoogle Scholar
  16. Qi, H. B., et al. (2010). Automated change detection for updating settlements at smaller-scale maps from updated larger-scale maps. Journal of Spatial Science, 55(1), 127–140.CrossRefGoogle Scholar
  17. Redweik, R., & Becker, T. (2015). Change detection in CityGML documents. In 3D Geoinformation science. Lecture Notes in Geoinformation and Cartography 2015 (pp. 107–121). Springer International Publishing. .Google Scholar
  18. Reed, C. (2005). The OpenGIS abstract specifications, Topic 0—Overview.Google Scholar
  19. Rehrl, K., & et al. (2013). A conceptual model for analyzing contribution patterns in the context of VGI. In J. Krisp (Ed.), Progress in location-based services. Lecture Notes in Geoinformation and Cartography (pp. 373–388). Springer, Berlin.Google Scholar
  20. Singh, A. (1989). Review article digital change detection techniques using remotely-sensed data. International Journal of Remote Sensing, 10(6), 989–1003.CrossRefGoogle Scholar
  21. Zielstra, D., et al. (2014). Areal delineation of home regions from contribution and editing patterns in OpenStreetMap. ISPRS International Journal of Geo-Information, 3(4), 1211–1233.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Karl Rehrl
    • 1
    Email author
  • Richard Brunauer
    • 1
  • Simon Gröchenig
    • 1
  1. 1.Salzburg Research Forschungsgesellschaft mbHSalzburgAustria

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