Tree Symbols Detection for Green Space Estimation
Geodetic base maps are very detailed sources of information. However, such maps are created for specialists and incomprehensible to non–professionals. An example of information that can be useful for citizen is change of urban green spaces. Such spaces, valuable for a local society can be destroyed by developers or a local government. Therefore, a monitoring of green areas is an important task that can be done on the basis of maps from Geodetic Documentation Centres. Unfortunately, the most popular form of digital documentations is a bitmap. This work presents a feasibility study of green areas estimation from scanned maps. The solution bases on symbols detection. Two kinds of symbols (coniferous and deciduous trees) are recognised by the following algorithm. Dots from centres of symbols are detected and their neighbourhood is extracted. Specific features are calculated as an input for neural networks that detect tree symbols. The accuracy of the detection is 90 percent, which is good enough to estimate green areas.
KeywordsMaps understanding image understanding image processing pattern recognition
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- 1.Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press (1995)Google Scholar
- 2.Chia, A., Leung, M., Eng, H.L., Rahardja, S.: Ellipse detection with hough transform in one dimensional parametric space. In: IEEE International Conference on Image Processing, ICIP 2007, vol. 5, pp. 333–336 (2007)Google Scholar
- 3.Choi, K.S., Ahn, D., Lee, S.H., Kong, Y.H.: Automatic recognition of map symbols for gis input module. In: 1997 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing, 10 Years PACRIM 1987-1997 - Networking the Pacific Rim, vol. 2, pp. 543–546 (1997)Google Scholar
- 8.Lu, W., Okuhashi, T., Sakauchi, M.: A proposal of efficient interactive recognition system for understanding of map drawings. In: Proceedings of the Third International Conference on Document Analysis and Recognition, vol. 1, pp. 520–523 (1995)Google Scholar
- 10.Mariani, R., Deseilligny, M.P., Labiche, J., Lecourtier, Y.: Geographic map understanding, attributes computation for hydrographic network. In: Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation, pp. 252–257 (1996)Google Scholar
- 11.Miller, R.W.: Urban forestry: Planning and managing urban greenspaces. Prentice-Hall (1997)Google Scholar
- 12.Stapor, K.: Geographic map image interpretation - survey and problems. Machine GRAPHICS & VISION 9(1/2), 497–518 (2000)Google Scholar
- 13.Szendrei, R., Elek, I., Fekete, I.: Automatic recognition of topographic map symbols based on their textures. In: Tan, Y., Shi, Y., Chai, Y., Wang, G. (eds.) ICSI 2011, Part II. LNCS, vol. 6729, pp. 299–306. Springer, Heidelberg (2011), http://dblp.uni-trier.de/db/conf/swarm/icsi2011-2.html#SzendreiEF11 CrossRefGoogle Scholar
- 14.Zhou, C.: Map recognition for automatic information generation in an am/fm system. In: Proceedings of the Intelligent Information Systems, IIS 1997, pp. 376–380 (1997)Google Scholar