Using Landsat-5 for Accurate Historical LULC Classification: A Comparison of Machine Learning Models

Denis Krivoguz, Sergei G. Chernyi (), Elena Zinchenko, Artem Silkin and Anton Zinchenko
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Denis Krivoguz: Department of the “Oceanology”, Southern Federal University, 340015 Rostov-on-Don, Russia
Sergei G. Chernyi: Department of Cyber-Physical Systems, St. Petersburg State Marine Technical University, Leninsky Prospect, 101, 198262 St. Petersburg, Russia
Elena Zinchenko: Department of Cyber-Physical Systems, St. Petersburg State Marine Technical University, Leninsky Prospect, 101, 198262 St. Petersburg, Russia
Artem Silkin: Department of Cyber-Physical Systems, St. Petersburg State Marine Technical University, Leninsky Prospect, 101, 198262 St. Petersburg, Russia
Anton Zinchenko: Department of Cyber-Physical Systems, St. Petersburg State Marine Technical University, Leninsky Prospect, 101, 198262 St. Petersburg, Russia

Data, 2023, vol. 8, issue 9, 1-17

Abstract: This study investigates the application of various machine learning models for land use and land cover (LULC) classification in the Kerch Peninsula. The study utilizes archival field data, cadastral data, and published scientific literature for model training and testing, using Landsat-5 imagery from 1990 as input data. Four machine learning models (deep neural network, Random Forest, support vector machine (SVM), and AdaBoost) are employed, and their hyperparameters are tuned using random search and grid search. Model performance is evaluated through cross-validation and confusion matrices. The deep neural network achieves the highest accuracy (96.2%) and performs well in classifying water, urban lands, open soils, and high vegetation. However, it faces challenges in classifying grasslands, bare lands, and agricultural areas. The Random Forest model achieves an accuracy of 90.5% but struggles with differentiating high vegetation from agricultural lands. The SVM model achieves an accuracy of 86.1%, while the AdaBoost model performs the lowest with an accuracy of 58.4%. The novel contributions of this study include the comparison and evaluation of multiple machine learning models for land use classification in the Kerch Peninsula. The deep neural network and Random Forest models outperform SVM and AdaBoost in terms of accuracy. However, the use of limited data sources such as cadastral data and scientific articles may introduce limitations and potential errors. Future research should consider incorporating field studies and additional data sources for improved accuracy. This study provides valuable insights for land use classification, facilitating the assessment and management of natural resources in the Kerch Peninsula. The findings contribute to informed decision-making processes and lay the groundwork for further research in the field.

Keywords: machine learning; LULC; Landsat; classification (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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