Using Landsat-5 for Accurate Historical LULC Classification: A Comparison of Machine Learning Models
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 fro...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Data |
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| Online Access: | https://www.mdpi.com/2306-5729/8/9/138 |
| _version_ | 1797580606226300928 |
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| author | Denis Krivoguz Sergei G. Chernyi Elena Zinchenko Artem Silkin Anton Zinchenko |
| author_facet | Denis Krivoguz Sergei G. Chernyi Elena Zinchenko Artem Silkin Anton Zinchenko |
| author_sort | Denis Krivoguz |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T22:53:14Z |
| format | Article |
| id | doaj.art-671720bac7a04c2197c70533d1d74fb1 |
| institution | Directory Open Access Journal |
| issn | 2306-5729 |
| language | English |
| last_indexed | 2024-03-10T22:53:14Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Data |
| spelling | doaj.art-671720bac7a04c2197c70533d1d74fb12023-11-19T10:11:39ZengMDPI AGData2306-57292023-08-018913810.3390/data8090138Using Landsat-5 for Accurate Historical LULC Classification: A Comparison of Machine Learning ModelsDenis Krivoguz0Sergei G. Chernyi1Elena Zinchenko2Artem Silkin3Anton Zinchenko4Department of the “Oceanology”, Southern Federal University, 340015 Rostov-on-Don, RussiaDepartment of Cyber-Physical Systems, St. Petersburg State Marine Technical University, Leninsky Prospect, 101, 198262 St. Petersburg, RussiaDepartment of Cyber-Physical Systems, St. Petersburg State Marine Technical University, Leninsky Prospect, 101, 198262 St. Petersburg, RussiaDepartment of Cyber-Physical Systems, St. Petersburg State Marine Technical University, Leninsky Prospect, 101, 198262 St. Petersburg, RussiaDepartment of Cyber-Physical Systems, St. Petersburg State Marine Technical University, Leninsky Prospect, 101, 198262 St. Petersburg, RussiaThis 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.https://www.mdpi.com/2306-5729/8/9/138machine learningLULCLandsatclassification |
| spellingShingle | Denis Krivoguz Sergei G. Chernyi Elena Zinchenko Artem Silkin Anton Zinchenko Using Landsat-5 for Accurate Historical LULC Classification: A Comparison of Machine Learning Models Data machine learning LULC Landsat classification |
| title | Using Landsat-5 for Accurate Historical LULC Classification: A Comparison of Machine Learning Models |
| title_full | Using Landsat-5 for Accurate Historical LULC Classification: A Comparison of Machine Learning Models |
| title_fullStr | Using Landsat-5 for Accurate Historical LULC Classification: A Comparison of Machine Learning Models |
| title_full_unstemmed | Using Landsat-5 for Accurate Historical LULC Classification: A Comparison of Machine Learning Models |
| title_short | Using Landsat-5 for Accurate Historical LULC Classification: A Comparison of Machine Learning Models |
| title_sort | using landsat 5 for accurate historical lulc classification a comparison of machine learning models |
| topic | machine learning LULC Landsat classification |
| url | https://www.mdpi.com/2306-5729/8/9/138 |
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