Comparison between random forest and support vector machine algorithms for LULC classification

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Avci C., Budak M., Yağmur N., Bektaş Balçık F.

International Journal of Engineering and Geosciences, vol.8, no.1, pp.1-10, 2023 (ESCI) identifier

  • Publication Type: Article / Article
  • Volume: 8 Issue: 1
  • Publication Date: 2023
  • Doi Number: 10.26833/ijeg.987605
  • Journal Name: International Journal of Engineering and Geosciences
  • Journal Indexes: Emerging Sources Citation Index (ESCI), Scopus, Central & Eastern European Academic Source (CEEAS), Directory of Open Access Journals, TR DİZİN (ULAKBİM)
  • Page Numbers: pp.1-10
  • Keywords: Random Forest, Remote sensing, Supervised Classification, Support Vector Machine, Wetland
  • Istanbul Technical University Affiliated: Yes


Nowadays, machine learning (ML) algorithms have been widely chosen for classifying satellite images for mapping Earth's surface. Support Vector Machine (SVM) and Random Forest (RF) stand out among these algorithms with their accurate results in the literature. The aim of this study is to analyze the performances of these algorithms on land use and land cover (LULC) classification, especially wetlands which have significant ecological functions. For this purpose, Sentinel-2 satellite image, which is freely provided by European Space Agency (ESA), was used to monitor not only the open surface water body but also around Marmara Lake. The performance evaluation was made with the increasing number of the training dataset. 3 different training datasets having 10, 15, and 20 areas of interest (AOI) per class, respectively were used for the classification of the satellite images acquired in 2015 and 2020. The most accurate results were obtained from the classification with RF algorithm and 20 AOIs. According to obtained results, the change detection analysis of Marmara Lake was investigated for possible reasons. Whereas the water body and wetland have decreased more than 50% between 2015 and 2020, crop sites have increased approximately 50%.