%0 Journal Article
%T Machine Learning Methods for Classification of the Green Infrastructure in City Areas
%J ISPRS International Journal of Geo-Information | An Open Access Journal from MDPI
%D 2019
%R https://doi.org/10.3390/ijgi8100463
%X 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 na£żve 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 (Vara£żdin and Osijek). Support vector machines outperform random forest, artificial neural networks, and the na£żve Bayes classifier in terms of classification accuracy (a Kappa value of 0.87 for Vara£żdin and 0.89 for Osijek) and performance time. View Full-Tex
%U https://www.mdpi.com/2220-9964/8/10/463