The Science and Information (SAI) Organization
  • Home
  • About Us
  • Journals
  • Conferences
  • Contact Us

Publication Links

  • IJACSA
  • Author Guidelines
  • Publication Policies
  • Digital Archiving Policy
  • Promote your Publication
  • Metadata Harvesting (OAI2)

IJACSA

  • About the Journal
  • Call for Papers
  • Editorial Board
  • Author Guidelines
  • Submit your Paper
  • Current Issue
  • Archives
  • Indexing
  • Fees/ APC
  • Reviewers
  • Apply as a Reviewer

IJARAI

  • About the Journal
  • Archives
  • Indexing & Archiving

Special Issues

  • Home
  • Archives
  • Proposals
  • Guest Editors
  • SUSAI-EE 2025
  • ICONS-BA 2025
  • IoT-BLOCK 2025

Future of Information and Communication Conference (FICC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Computing Conference

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Intelligent Systems Conference (IntelliSys)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Future Technologies Conference (FTC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact
  • Home
  • Call for Papers
  • Editorial Board
  • Guidelines
  • Submit
  • Current Issue
  • Archives
  • Indexing
  • Fees
  • Reviewers
  • Subscribe

DOI: 10.14569/IJACSA.2020.0110573
PDF

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

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

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

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}
}



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.

IJACSA

Upcoming Conferences

IntelliSys 2025

28-29 August 2025

  • Amsterdam, The Netherlands

Future Technologies Conference 2025

6-7 November 2025

  • Munich, Germany

Healthcare Conference 2026

21-22 May 2026

  • Amsterdam, The Netherlands

Computing Conference 2026

9-10 July 2026

  • London, United Kingdom

IntelliSys 2026

3-4 September 2026

  • Amsterdam, The Netherlands

Computer Vision Conference 2026

15-16 October 2026

  • Berlin, Germany
The Science and Information (SAI) Organization
BACK TO TOP

Computer Science Journal

  • About the Journal
  • Call for Papers
  • Submit Paper
  • Indexing

Our Conferences

  • Computing Conference
  • Intelligent Systems Conference
  • Future Technologies Conference
  • Communication Conference

Help & Support

  • Contact Us
  • About Us
  • Terms and Conditions
  • Privacy Policy

© The Science and Information (SAI) Organization Limited. All rights reserved. Registered in England and Wales. Company Number 8933205. thesai.org