期刊
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
卷 8, 期 10, 页码 -出版社
MDPI
DOI: 10.3390/ijgi8100463
关键词
green urban infrastructure; support vector machines; artificial neural networks; naive Bayes classifier; random forest; Sentinel 2-MSI
资金
- Croatian Science Foundation [IP-2016-06-5621]
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 naive 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 (Varazdin and Osijek). Support vector machines outperform random forest, artificial neural networks, and the naive Bayes classifier in terms of classification accuracy (a Kappa value of 0.87 for Varazdin and 0.89 for Osijek) and performance time.
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