• Harsh K. Shinde
• Dr. Rahul A. Burange
• Omkar Mutyalwar

Landslides pose severe threats to infrastructure, economies, and human lives, necessitating accurate detection and predictive mapping across diverse geographic regions. With advancements in deep learning and remote sensing, automated landslide detection has become increasingly effective. This paper explores the integration of remote sensing and geospatial data to enhance landslide identification, focusing on Sentinel-2 multispectral imagery and ALOS PALSAR-derived slope and Digital Elevation Model (DEM) data. By leveraging these datasets, we analyse key environmental factors such as vegetation cover, rainfall, and terrain characteristics to improve landslide mapping accuracy. The study also evaluates various geospatial analysis techniques to assess their impact on detection performance. The findings provide valuable insights for early warning systems, disaster mitigation, and land-use planning, contributing to the development of more reliable and scalable landslide prediction frameworks.

Image Processing, Machine Learning, Deep Learning, Computer Vision, Remote Sensing.

"An Exhaustive Review on Deep Learning for Advanced Landslide Detection and Prediction from Multi-Source Satellite Imagery", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 2, page no.g205-g211, February-2025, Available :http://www.jetir.org/papers/JETIR2502619.pdf

"An Exhaustive Review on Deep Learning for Advanced Landslide Detection and Prediction from Multi-Source Satellite Imagery", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 2, page no. ppg205-g211, February-2025, Available at : http://www.jetir.org/papers/JETIR2502619.pdf