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@article{172126,
        author = {Pradnya P. Parate and Shashank Shriramwar},
        title = {COMPARATIVE ANALYSIS ON SATELLITE IMAGERY CLASSIFICATION AND OBJECT SEGMENTATION BASED ON COMPUTER VISION AND ARTIFICIAL INTELLIGENCE},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {8},
        pages = {1965-1973},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=172126},
        abstract = {Satellite imaging has become an essential tool for observing the Earth's surface, facilitating many uses including urban growth, environmental preservation, and disaster response. The escalating amount and resolution of satellite imagery need the development of efficient and precise methodologies for image classification and object segmentation. This study offers a comparative review of several Computer Vision (CV) and Artificial Intelligence (AI) methodologies for the classification and segmentation of satellite data. The exploration encompasses various traditional machine learning methods alongside advanced deep learning techniques, including Convolutional Neural Networks (CNNs), U-Net, and Vision Transformers. Performance criteria like as accuracy, Intersection over Union (IoU), and F1 score are employed to assess these algorithms on benchmark datasets. The research delineates the advantages and drawbacks of each methodology, offering insights into their relevance for certain applications in satellite image analysis.},
        keywords = {Remote Sensing, Artificial Intelligence, Satellite Imagery, Deep Learning, Machine Learning.},
        month = {January},
        }