Predicting Landslides through Satellite Imagery Analysis and Machine Learning


  • Anup Kadu, Raj Mishra, Vishal Shirsath


Landslide Prediction, Satellite Imagery Analysis, Machine Learning Algorithms, Digital Elevation Model (DEM), Geotechnical Hazard Assessment


The effects of climate change on landslides become more apparent, this work presents a novel method of landslide prediction by combining cutting-edge machine learning algorithms with Google Earth satellite images. Using digital image processing and Geographic Information System (GIS) techniques, the proposed method extracts important parameters, like elevation and slope, from high-resolution satellite data. Landslides are becoming a critical threat due to their increasing frequency, necessitating accurate prediction and early warning systems. Then, an intricate digital elevation model (DEM) is created and utilized as an input for more complex machine learning models, such as CNN and polygonal neural networks. Precise prediction of probable landslide events across large, susceptible areas is made possible by this novel combination. Landslides may have a major negative impact on human life and the economy, but the integrated method greatly improves the accuracy of early detection. The results demonstrate the efficacy of this innovative approach in delivering precise and timely forecasts, signifying a significant advancement in the evaluation of geotechnical hazards and proactive risk control for expansive, high-risk regions. In order to meet the urgent need for proactive mitigation in the face of climate-induced risks, this research presents a strong foundation for comprehensive landslide prediction.


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How to Cite

Raj Mishra, Vishal Shirsath, A. K. . (2024). Predicting Landslides through Satellite Imagery Analysis and Machine Learning. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 974–985. Retrieved from



Research Article