Abstract
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.
The original version of this chapter was revised: The copyright line was incorrect. This has been corrected. The Erratum to this chapter is available at DOI: 10.1007/978-3-319-02895-8_64
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Sroka, A., Luckner, M. (2013). Tree Symbols Detection for Green Space Estimation. In: Blanc-Talon, J., Kasinski, A., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2013. Lecture Notes in Computer Science, vol 8192. Springer, Cham. https://doi.org/10.1007/978-3-319-02895-8_47
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DOI: https://doi.org/10.1007/978-3-319-02895-8_47
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