Comparative Study on Various Supervised Learning Algorithms for Land Use and Land Cover Classification in Part of East Godavari District, Andhra Pradesh, India
DOI:
https://doi.org/10.32628/CSEIT2511148Keywords:
LULC, Supervised Learning, Remote Sensing, Machine Learning, East Godavari, Classification AccuracyAbstract
Accurate Land Use and Land Cover (LU/LC) classification is essential for sustainable resource management, urban development, and environmental conservation. The integration of remote sensing data with supervised machine learning algorithms has significantly enhanced classification accuracy and efficiency. This study evaluates the performance of five widely used supervised learning algorithms namely 1) Classification and Regression Tree (CART), 2) Gradient Boost Tree (GB), 3) K-Nearest Neighbours (KNN), 4) Support Vector Machine (SVM) and 5) Random Forest (RF) for LU/LC mapping in study area of East Godavari District, Andhra Pradesh, India over a time period of 2 years between 2023 and 2025. High-resolution Landsat-8 imagery is processed and classified using above algorithms, with model performance assessed based on overall accuracy, Kappa coefficient, precision and F1-score. The findings indicated that Gradient Tree Boost demonstrated superior performance compared to the other classifiers, attaining the highest accuracy of 98.26% along with a Kappa coefficient of 0.9761. Random Forest closely followed, achieving an accuracy of 97.39% and a Kappa value of 0.9642. Additionally, both SVM and KNN exhibited strong classification capabilities, with respective accuracies of 96.52% and Kappa values of 0.9522, highlighting their effectiveness in land cover classification applications. The study also examines the computational efficiency and reliability of each classifier, offering insights into their suitability for LU/LC analysis in diverse landscapes. The findings contribute to the optimization of machine learning techniques for remote sensing applications, aiding in data-driven decision-making for land management. Future research can explore deep learning-based classification models and multi-temporal analysis to further enhance LU/LC mapping accuracy.
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