A Multi-parameter Approach to Automated Building Grouping and Generalization
Rent the article at a discountRent now
* Final gross prices may vary according to local VAT.Get Access
This paper presents an approach to automated building grouping and generalization. Three principles of Gestalt theories, i.e. proximity, similarity, and common directions, are employed as guidelines, and six parameters, i.e. minimum distance, area of visible scope, area ratio, edge number ratio, smallest minimum bounding rectangle (SMBR), directional Voronoi diagram (DVD), are selected to describe spatial patterns, distributions and relations of buildings. Based on these principles and parameters, an approach to building grouping and generalization is developed. First, buildings are triangulated based on Delaunay triangulation rules, by which topological adjacency relations between buildings are obtained and the six parameters are calculated and recorded. Every two topologically adjacent buildings form a potential group. Three criteria from previous experience and Gestalt principles are employed to tell whether a 2-building group is ‘strong,’ ‘average’ or ‘weak.’ The ‘weak’ groups are deleted from the group array. Secondly, the retained groups with common buildings are organized to form intermediate groups according to their relations. After this step, the intermediate groups with common buildings are aggregated or separated and the final groups are formed. Finally, appropriate operators/algorithms are selected for each group and the generalized buildings are achieved. This approach is fully automatic. As our experiments show, it can be used primarily in the generalization of buildings arranged in blocks.
- Bader, M., Barrault, M., Weibel, R. (2005) Building displacement over a ductile truss. International Journal of Geographical Information Science 19: pp. 915-936 CrossRef
- M. Bader and R. Weibel. “Detecting and resolving size and proximity conflicts in the generalisation of polygon maps,” in Proceedings of the 18th International Cartographic Conference, pp. 1525–1532, Stockholm, Sweden, 1997.
- A. Boffet and S. Rocca Serra. “Identification of spatial structures within urban blocks for town characterisation,” in Proceedings of the 20th International Cartographic Conference, Beijing, China, 2001 (CD-ROM).
- Christophe, S., Ruas, A. Detecting building alignments for generalisation purposes. In: Richardson, D.E., Oosterom, P. eds. (2002) Advances in Spatial Data Handling (10th International Symposium on Spatial Data Handling). Springer, Berlin Heidelberg New York, pp. 419-432
- C. Duchêne, S. Bard, and X. Barillot. “Quantitative and qualitative description of building orientation,” in The 5th ICA workshop on progress in automated map generalization, Paris, France, 2003. http://www.geo.unizh.ch/ICA/docs/paris2003/papers/duchene_et_al_v1.pdf.
- R.K. Goyal. “Similarity assessment for cardinal directions between extended spatial objects,” PhD thesis, The University of Maine, 2000.
- Jones, C.B., Bundy, G.L., Ware, J.M. (1995) Map generalization with a triangulated data structure. Cartography and Geographic Information Systems 22: pp. 317-331
- Jones, C.B., Ware, J.M. (2005) Map generalization in the web age. International Journal of Geographical Information Science 19: pp. 859-870 CrossRef
- Li, Z., Yan, H., Ai, T. (2004) Automated building generalization based on urban morphology and gestalt theory. International Journal of Geographical Information Science 18: pp. 513-534 CrossRef
- McMaster, R.B., Shea, K.S. (1992) Generalization in Digital Cartography. Association of American Cartographers, Washington DC
- Palmer, S.E. (1992) Common region: a new principle of perceptual grouping. Cognitive Psychology 24: pp. 436-447 CrossRef
- Papadias, D., Sellis, T. (1994) The qualitative representation of spatial knowledge in two dimensional space. Very Large Database Journal 3: pp. 479-516 CrossRef
- Peuquet, D., Zhan, C.X. (1987) An algorithm to determine the directional relationship between arbitrarily-shaped polygons in the plane. Pattern Recognition 20: pp. 65-74 CrossRef
- Rainsford, D., Mackaness, W. Template matching in support of generalization of rural buildings. In: Richardson, D.E., Oosterom, P. eds. (2002) Advances in Spatial Data Handling (10th International Symposium on Spatial Data Handling). Springer, Berlin Heidelberg New York, pp. 137-151
- Regnauld, N. (2001) Contextual building typification in automated map generalization. Algorithmica 30: pp. 312-333 CrossRef
- Rock, I (1996) Indirect Perception. MIT Press, London
- Ruas, A (1998) A method for building displacement in automated map generalization. International Journal of Geographical Information Science 12: pp. 789-803 CrossRef
- A. Ruas and C. Plazanet. “Strategies for automated generalization,” in Proceedings of Spatial Data Handling, pp. 6.1–6.18, 1996.
- Shekhar, S., Liu, X., Chawla, S. (1999) An object model of direction and its application. Geoinformatica 3: pp. 357-379 CrossRef
- Steinhauer, J.H., Wiese, T., Freksa, C., Barkowsky, T. Recognition of abstract regions in cartographic maps. In: Montello, D.R. eds. (2001) Spatial Information Theory. Springer, Berlin Heidelberg New York, pp. 306-321 CrossRef
- SSC. Topographic Maps: Map Graphics and Generalization, Cartographic Publication Series No. 17. Swiss Society of Cartography, 2005 (CD-ROM).
- Weibel, R. A typology of constraints to line simplification. In: Kraak, M.J., Molenaar, M. eds. (1996) Advances on GIS II. Taylor & Francis, London, pp. 9A.1-9A.14
- Yan, H.W., Chu, Y.D., Li, Z.L., Guo, R.Z. (2006) A quantitative description model for directional relations based on direction groups. Geoinformatica 10: pp. 177-195 CrossRef
- Yukio, S. (1997) Cluster perception in the distribution of point objects. Cartographica 34: pp. 49-61
- A Multi-parameter Approach to Automated Building Grouping and Generalization
Volume 12, Issue 1 , pp 73-89
- Cover Date
- Print ISSN
- Online ISSN
- Springer US
- Additional Links
- Gestalt principles
- building grouping
- directional relations
- map generalization
- Industry Sectors
- Author Affiliations
- 1. School of Mathematics, Physics and Software Engineering, Lanzhou Jiaotong University, Lanzhou, 730070, China
- 2. GIS Division, Department of Geography, University of Zurich, Zurich, Switzerland
- 3. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, 430079, China