Application Of The Machine Learning (ML) Approaches Based On Classification Tools In Predicting Of Urban Land Cover
Tejas Thakral
Vivekanand Institute of Professional Studies, New Delhi
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Understanding the evolution of the urban environment and its implications requires an understanding of the classification of urban land cover. In this work, we present a comparative examination of machine learning techniques for urban land cover classification using remote sensing data. The research makes use of the UCI Machine Learning Repository's Urban Land Cover dataset, which is made up of high-resolution photos of cities. Together with their accuracy scores, a comparison of a number of well-known machine learning classification algorithms is also conducted, including the Decision Tree, Random Forest, Support Vector Machine, XGBoost, K-Nearest Neighbors, and Ridge classifiers. After eliminating the outliers and fine-tuning the hyper-parameters using Grid Search CV, the Random Forest algorithm beats the other machine learning algorithms, with an overall accuracy of 91.38%.
Keywords:
machine learning; Urban land cover; modifiable areal unit problem (MAUP)
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