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@article{168108,
        author = {Vijay Krishnan MR and Dr Raghav Mehra and Dr. Hari Prasada Raju Kunadharaju},
        title = {Classical Machine Learning as a Novel Approach for Land Cover Mapping},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {4},
        pages = {1305-1312},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=168108},
        abstract = {The classification of land use and land cover is essential for understanding and monitoring changes in the Earth's surface over time. This literature review systematically explores the application of classical machine learning techniques in land use and land cover classification, highlighting their effectiveness in accurately identifying and categorizing different types of land. These methods are indispensable in urban planning, environmental monitoring, and resource management, contributing significantly to sustainable development practices. The survey delves into various classical machine learning approaches, such as decision trees, support vector machines, random forests, and k-nearest neighbors, examining their applications, efficacy, and limitations. It also discusses current challenges faced in this research area, including issues related to data quality, computational demands, and algorithmic selection, and forecasts future directions with potential advancements in technology and methodology. This review aims to provide insights that aid in the advancement of more accurate and efficient classification systems.},
        keywords = {Land Use Classification, Land Cover Classification, Classical Machine Learning, Techniques, Environmental Monitoring, Urban Planning, Decision Trees, Support Vector Machines, Random Forests, k-Nearest Neighbors, Predictive Analytics},
        month = {September},
        }