文章基本信息

标题：Urbanization Change Analysis based on SVM and RF Machine Learning Algorithms
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作者：Farhad Hassan ; Tauqeer Safdar ; Ghulam Irtaza 等
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2020
卷号：11
期号：5
DOI：10.14569/IJACSA.2020.0110573
出版社：Science and Information Society (SAI)
摘要：To maintain sustainability in the development, measured the yearly change rate of the land through Land Cover classified maps that hold the data which is surveyed as an influential factor for environment management and urbanization. This paper measured the change rate, which is helpful for the management of the city to define the new policy and implement the best one to maintain the natural resources. Machine Learning algorithms are utilized to produce the most acknowledged Land Cover maps using the GEE cloud-based reliable platform using the LANDSAT8 satellite imagery. For the classification used the Random Forest (RF) and Support Vector Machine (SVM) Algorithm. This investigation also found that the Support Vector Machine (SVM) classifier accomplished better over-all accuracy and Kappa coefficient as compared to the Random Forest (RF) classifier while the training sample for both is the same.
关键词：Random Forest (RF); Support Vector Machine (SVM); GEE; classification; machine learning classifier; multi-temporal change analysis; urban change analysis; LANDSAT8; Kappa co-efficient