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Machine Learning Methods for Classification of the Green Infrastructure in City Areas

1
Faculty of Geotechnical Engineering, University of Zagreb, Hallerova aleja 7, 42000 Varaždin, Croatia
2
Faculty of Geodesy, University of Zagreb, Kačićeva 26, 10000 Zagreb, Croatia
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(10), 463; https://doi.org/10.3390/ijgi8100463
Received: 23 August 2019 / Revised: 17 September 2019 / Accepted: 21 October 2019 / Published: 22 October 2019
Rapid urbanization in cities can result in a decrease in green urban areas. Reductions in green urban infrastructure pose a threat to the sustainability of cities. Up-to-date maps are important for the effective planning of urban development and the maintenance of green urban infrastructure. There are many possible ways to map vegetation; however, the most effective way is to apply machine learning methods to satellite imagery. In this study, we analyze four machine learning methods (support vector machine, random forest, artificial neural network, and the naïve Bayes classifier) for mapping green urban areas using satellite imagery from the Sentinel-2 multispectral instrument. The methods are tested on two cities in Croatia (Varaždin and Osijek). Support vector machines outperform random forest, artificial neural networks, and the naïve Bayes classifier in terms of classification accuracy (a Kappa value of 0.87 for Varaždin and 0.89 for Osijek) and performance time. View Full-Text
Keywords: green urban infrastructure; support vector machines; artificial neural networks; naïve Bayes classifier; random forest; Sentinel 2-MSI green urban infrastructure; support vector machines; artificial neural networks; naïve Bayes classifier; random forest; Sentinel 2-MSI
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Kranjčić, N.; Medak, D.; Župan, R.; Rezo, M. Machine Learning Methods for Classification of the Green Infrastructure in City Areas. ISPRS Int. J. Geo-Inf. 2019, 8, 463.

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