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@article{171860,
        author = {Nandini Parihar and Ajay Kumar and Aryan Chaudhary and Garima Singh and Jatin Singh Shani and Kaustub Dhondiyal},
        title = {Review of Remote Sensing Methods and Machine Learning Applications for Landslide Detection and Risk Assessment},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {8},
        pages = {1251-1257},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=171860},
        abstract = {Landslides are one of the most significant natural hazards that cause extensive damage to life, property, and infrastructure worldwide [3]. Advances in geospatial technologies, remote sensing, machine learning (ML), and deep learning (DL) have been found to be effective for landslide detection, susceptibility mapping, and risk prediction. This review consolidates the insights from multiple studies into traditional and modern techniques used for landslide monitoring while throwing light on future research directions [1]. It focuses on integrating ML and DL approaches with remote sensing to upgrade early warning systems and strategies of risk mitigation.},
        keywords = {},
        month = {January},
        }