Review of Remote Sensing Methods and Machine Learning Applications for Landslide Detection and Risk Assessment

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Nandini Parihar; Ajay Kumar; Aryan Chaudhary; Garima Singh; Jatin Singh Shani; Kaustub Dhondiyal

Review of Remote Sensing Methods and Machine Learning Applications for Landslide Detection and Risk Assessment

Authors: Nandini Parihar, Ajay Kumar, Aryan Chaudhary, Garima Singh, Jatin Singh Shani, Kaustub Dhondiyal

Unique Paper ID: 171860
Volume: 11
Issue: 8
PageNo: 1251-1257

Keywords: No Keywords Found

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.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 8
PageNo: 1251-1257

Review of Remote Sensing Methods and Machine Learning Applications for Landslide Detection and Risk Assessment

Available:https://ijirt.org/article?manuscript=171860

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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