Abstract
The most commonly used model for analyzing satellite imagery is the Support Vector Machine (SVM). Since there are a large number of possible variables for use in SVM, this paper will provide a combination of parameters that fit best for extracting green urban areas from Copernicus mission satellite images. This paper aims to provide a combination of parameters to extract green urban areas with the highest degree of accuracy, in order to speed up urban planning and ultimately improve town environments. Two different towns in Croatia were investigated, and the results provide an optimal combination of parameters for green urban areas extraction with an overall kappa index of 0.87 and 0.89, which demonstrates a very high classification accuracy.
Highlights
The green urban areas infrastructure approach considers maximizing physical and functional connectivity while optimizing multi-functionality in terms of social, ecological, and economic benefits [1], as well as resilience through landscape diversity [2,3].Green urban areas are important for urban planning and improving the urban environment [4]
Two different towns in Croatia were investigated, and the results provide an optimal combination of parameters for green urban areas extraction with an overall kappa index of 0.87 and 0.89, which demonstrates a very high classification accuracy
Urban planning for and increasing the amount of green urban infrastructure are crucial for healthier living
Summary
The green urban areas infrastructure approach considers maximizing physical and functional connectivity while optimizing multi-functionality in terms of social, ecological, and economic benefits [1], as well as resilience through landscape diversity [2,3].Green urban areas are important for urban planning and improving the urban environment [4]. Satellite imagery is the fastest method for data collection for urban planning. The authors in [9] used GeoEye-1 high spatial resolution satellite data to map canopy mortality caused by a pine beetle outbreak. They concluded that high-resolution imagery is a useful tool to map such natural disasters. The authors in [14] used unsupervised machine learning to map landscape soils and vegetation components from satellite imagery. The authors in [15] used machine learning classification in order to map vegetation and land use types. As seen from the abovementioned literature, a lot of work has been done with machine learning to extract vegetation information
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