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@article{175742,
        author = {K. Pavan Kumar and A. N. Dinesh Kumar},
        title = {Artificial Intelligence Techniques For Landslides Prediction Using Satellite Imagery},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {3939-3941},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175742},
        abstract = {Landslides in hilly areas can occur due to natural factors like heavy rainfall, earthquakes, and soil moisture, or man-made causes such as unplanned construction. These events can cause significant damage to property and loss of life. Machine learning (ML) algorithms are increasingly being used for automatic landslide prediction and detection, especially using satellite images. Despite progress, challenges remain in achieving fully automatic detection with high accuracy due to the difficulty in obtaining appropriate training datasets. This study reviews fifty papers on ML and deep learning techniques for landslide classification, aiming to identify gaps in research. A comparison of various methods is presented, highlighting their accuracy. Based on these findings, a novel approach using a modified version of the deep learning model ResNet101 is proposed, achieving a 96.88% accuracy when tested on an augmented Beijing dataset of 770 satellite images. The article provides a comprehensive overview of the current state of landslide detection using ML and deep learning, and suggests potential areas for further research.},
        keywords = {},
        month = {April},
        }