Neural Processing Letters

, Volume 41, Issue 2, pp 273–292 | Cite as

Local Perception-Based Intelligent Building Outline Aggregation Approach with Back Propagation Neural Network

  • Boyan Cheng
  • Qiang Liu
  • Xiaowen Li


With the analysis of the characteristics of back propagation neural network (BPNN) and map generalization of building outlines in both urban and suburban areas, a new approach that is based on local perception of map contexts for building aggregation has been studied. The method called the local perception-based intelligent building outline aggregation approach with BPNN technique consisted of two BPNNs. \(\text {BPNN}_{1}\) was developed to generate initial aggregated building outlines. A circular detector coupled with a set of mapping rules was designed to detect buildings from raster maps. Once trained, \(\text {BPNN}_{1}\) produced initial aggregated building outlines. Due to the existence of unwanted nodes forming small steps along the outlines, \(\text {BPNN}_{2}\) was created to remove the unwanted ones. Here, a square detector was designed and a set of refining rules formulated. Together, \(\text {BPNN}_{1}\) and \(\text {BPNN}_{2}\) intelligently delineated individual buildings or a group of buildings. The performance of the approach has been assessed and the generalized results were cartographically satisfactory.


Back propagation (BP) Building aggregation Map generalization Neural network (NN) Urban and suburban areas 



This research was partially funded by the National Natural Science Foundation of China under Grant No. 41071222 to the University of Electronic Science and Technology, China.


  1. 1.
    ICA (International Cartographic Association) (1973) Multilingual dictionary of technical terms in cartography. Franz Steiner Verlag GmbH, Wiesbaden, Germany 573 pGoogle Scholar
  2. 2.
    Li Z, Yan H, Ai T, Chen J (2004) Automated building generalization based on urban morphology and gestalt theory. Int J Geogr Inf Sci 18(5):513–534CrossRefGoogle Scholar
  3. 3.
    Peng W (1997) Automatic generalization in GIS. ITC Publication Series, EnschedeGoogle Scholar
  4. 4.
    Wu HH (2000) Problems of city plan generalization in the GIS environment. J Wuhan Tech Univ Surv Mapp 25(3):196–202Google Scholar
  5. 5.
    Qian HZ, Wu F (2001) A merge operation for area objects based on Delaunay triangle-interpolating. J Inst Surv Mapp 18(3):207–209Google Scholar
  6. 6.
    Regnauld N (1996) Recognition of building cluster for generalization. Proceedings of the 7th international symposium on spatial data handling (SDH’96), pp 185–198Google Scholar
  7. 7.
    Ai TH, Guo RZ, Chen XD (2001) Simplification and aggregation of polygon object supported by Delaunay triangulation structure. J Image Gr 6A(7)” 703–709Google Scholar
  8. 8.
    Ai TH, Guo RZ (2002) A constrained Delaunay partitioning of areal objects to support map generalization. J Wuhan Techn Univ Surv Mapp 25(1):35–41Google Scholar
  9. 9.
    Ruas A (1995) Multiple paradigms for automating map generalization: geometry, topology, hierarchical partitioning and local triangulation. American Congress on Surveying and Mapping, American Society for Photogrammetry and Remote Sensing (ACSM /ASPRS’95) AutoCarto 12, pp 69–78Google Scholar
  10. 10.
    Ware JM, Jones CB (1996) A spatial model for detecting (and resolving) conflict caused by scale reduction. Proceedings of the 7th international symposium on spatial data handling (SDH’96), pp 547–558Google Scholar
  11. 11.
    Qian HZ, Wu F, Tan X, Deng HY (2005) The algorithm for merging city buildings based on ABTM. J Image Gr 10(10):1224–1233Google Scholar
  12. 12.
    Zhang J, Zhou Y, Liu Y (2006) An improved algorithm for SDS model based polygon simplification and aggregation. J Image Gr 11(7):1011–1016Google Scholar
  13. 13.
    Meng L (1997) Automatic generalization of geographic data [online]. Technical Report, VBB Viak, Stockholm, Sweden. Available from: Accessed July 2012
  14. 14.
    Li J, Zheng SY, Zhang ZX, Yu H, Liu JB (2005) Buildings generalization based on mathematical morphology. Proceeding of the society for optical engineering (SPIE 2005), MIPPR 2005: SAR and Multispectral Image Processing 6043, pp 372–377Google Scholar
  15. 15.
    Guo RZ, Ai TH (2000) Simplification and aggregation of building polygon in automatic map generalization. J Wuhan Techn Univ Surv Mapp 25(1):25–30Google Scholar
  16. 16.
    Ware JM (2003) Automated map generalization with multiple operators: a simulated annealing approach. Int J Geogr Inform Sci 17(8):743–769CrossRefGoogle Scholar
  17. 17.
    Jiang B, Harrie L (2004) Selection of streets from a network using self-organizing map. Trans GIS 8(3):335–350CrossRefGoogle Scholar
  18. 18.
    Allouche MK, Moulin B (2005) Amalgamation in cartographic generalization using Kohonen’s feature nets. Int J Geogr Inform Sci 19(8–9):899–914CrossRefGoogle Scholar
  19. 19.
    Steiniger S, Taillandier P, Weibel R (2010) Utilising urban context recognition and machine learning to improve the generalisation of buildings. Int J Geogr Inform Sci 24(2):253–282CrossRefGoogle Scholar
  20. 20.
    Su B, Li Z, Lodwick G, Mulier J (1997) Algebraic models for the aggregation of area features based upon morphological operators. Int J Geogr Inform Sci 11(3):233–246CrossRefGoogle Scholar
  21. 21.
    Lek S, Guégan JF (1999) Artificial neural networks as a tool in ecological modelling, an introduction. Ecol Model 120:65–73CrossRefGoogle Scholar
  22. 22.
    Sukthankar R, Pomerleau D, Thorpe C (1993) Panacea: an active sensor controller for the ALVINN autonomous driving system. Technical Report CMU-RI-TR-93-09, Robotics Institute (NAVLAB), Pittsburgh Carnegie Mellon UniversityGoogle Scholar
  23. 23.
    Wang HL, Wu F, Zhang LL, Deng HY (2005) The application of mathematical morphology and pattern recognition to building polygon simplification. Acta Geodaetica et Cartographica Sinica 34(3):269–276Google Scholar
  24. 24.
    Basaraner M, Selcuk M (2008) A structure recognition technique in contextual generalisation of buildings and built-up areas. Cartogr J 45(4):274–285CrossRefGoogle Scholar
  25. 25.
    Pawlus W, Karimi HR, Robbersmyr KG (2013) Data-based modeling of vehicle collisions by nonlinear autoregressive model and feedforward neural network. Inform Sci 235:65–79CrossRefGoogle Scholar
  26. 26.
    Karimi HR, Robbersmyr KG (2011) Signal analysis and performance evaluation of a vehicle crash test with a fixed safety barrier based on Haar wavelets. Int J Wavel Multiresolout Image Process 9(1):131–149CrossRefMATHGoogle Scholar
  27. 27.
    Gao ZS, Shi P, Karimi HR, Pei Z (2013) A mutual GrabCut method to solve co-segmentation. EURASIP J Image Video Process 2013: 20Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.School of Resources and EnvironmentUniversity of Electronic Science and Technology of ChinaChengdu People’s Republic of China

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