Abstract: Landslides present significant risks to infrastructure, economies, and human safety, requiring advanced detection and predictive mapping strategies. This study explores the integration of deep learning and remote sensing techniques to enhance landslide identification. Utilizing Sentinel-2 multispectral imagery and ALOS PALSAR-derived slope and Digital Elevation Model (DEM) data, the research examines critical environmental factors such as vegetation cover, rainfall, and terrain features. Additionally, various geospatial analysis techniques are evaluated to determine their effectiveness in improving detection accuracy. The findings contribute to the advancement of early warning systems, disaster risk management, and sustainable land-use planning, fostering more reliable and scalable landslide prediction models.
Keywords: - Image Processing, Machine Learning, Deep Learning, Computer Vision, Remote Sensing.
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DOI:
10.17148/IJARCCE.2025.14346
