Abstract
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.
Highlights
Since more and more people are living in cities, it has become extremely important to plan and manage green urban areas
The study areas are located in two towns in Croatia: Varaždin and Osijek
Under column support vector machines (SVMs) (Support Vector Machine) and NB there is only one record because for SVM is in this paper presented only the best combination of parameters from [15], while for NB there are no combination of parameter available as mentioned previously in text
Summary
Since more and more people are living in cities, it has become extremely important to plan and manage green urban areas. The authors in [11] compared artificial neural networks with random forest and SVMs for crop classification using high-resolution RapidEye imagery They concluded that SVM outperformed the other two machine learning methods. The authors in [15] explored how different kernels affect the land cover classification results using Sentinel-2 imagery They found that the radial basis function produced the highest accuracy and proposed further research using different machine learning methods. The authors in [15] concluded that the RBF kernel provides the best results with γ = 1 and C = 28, where γ is the free parameter of the radial basis function and C is parameter that allows to trade off training error versus model complexity They made conclusion based on experiment on two cities in Croatia on Sentinel 2 imagery. Classification accuracy rank was modified from [34]
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