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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Urbanization Change Analysis based on SVM and RF Machine Learning Algorithms

Author 1: Farhad Hassan
Author 2: Tauqeer Safdar
Author 3: Ghulam Irtaza
Author 4: Aman Ullah Khan
Author 5: Syed Muhammad Husnain Kazmi
Author 6: Farah Murtaza

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2020.0110573

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 5, 2020.

  • Abstract and Keywords
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Abstract: 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.

Keywords: Random Forest (RF); Support Vector Machine (SVM); GEE; classification; machine learning classifier; multi-temporal change analysis; urban change analysis; LANDSAT8; Kappa co-efficient

Farhad Hassan, Tauqeer Safdar, Ghulam Irtaza, Aman Ullah Khan, Syed Muhammad Husnain Kazmi and Farah Murtaza, “Urbanization Change Analysis based on SVM and RF Machine Learning Algorithms” International Journal of Advanced Computer Science and Applications(IJACSA), 11(5), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110573

@article{Hassan2020,
title = {Urbanization Change Analysis based on SVM and RF Machine Learning Algorithms},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110573},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110573},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {5},
author = {Farhad Hassan and Tauqeer Safdar and Ghulam Irtaza and Aman Ullah Khan and Syed Muhammad Husnain Kazmi and Farah Murtaza}
}


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