• S Rakesh
• Dr. A. Ganesh

Landslides are among the most destructive natural disasters, often triggered by factors such as rainfall, seismic activity, deforestation, and slope instability. Accurate and timely prediction of landslides is critical to minimizing human and economic losses. This research proposes a novel framework that leverages artificial intelligence (AI) and satellite imagery for early landslide prediction. The approach integrates multispectral and high-resolution satellite data with advanced machine learning techniques, such as Convolutional Neural Networks (CNNs), to extract spatial features associated with potential landslide zones. The model is trained and validated on historical landslide datasets and geospatial information, ensuring robust performance across diverse terrains. By analyzing topographic, vegetation, and soil-related indicators from satellite images, the proposed system effectively identifies high-risk zones. This AI-driven solution enhances disaster preparedness by offering a scalable, real-time, and automated method for landslide risk assessment. The outcomes demonstrate improved prediction accuracy, supporting government agencies and disaster management authorities in proactive planning and response strategies. The proposed system incorporates preprocessing techniques such as noise reduction, contrast enhancement, and normalization to improve the quality of satellite imagery before feeding it into the model. Using annotated landslide inventories, the deep learning model is trained to learn complex spatial patterns and terrain features indicative of landslide-prone areas. Features such as slope gradient, soil moisture, vegetation density (NDVI), and rainfall patterns are extracted and analyzed. By applying data fusion techniques, both optical and radar satellite data are combined to improve prediction accuracy under varying weather and lighting conditions. This AI-based framework offers a significant advancement over traditional empirical and statistical methods that often fail to generalize across regions. The model's capability to operate at scale, with minimal human intervention, makes it ideal for real-time monitoring and early warning systems. Furthermore, its deployment through web or cloud platforms can assist local authorities, urban planners, and environmental agencies in decision-making. The study's results highlight the potential of integrating AI with satellite technology to build more resilient infrastructures and reduce landslide-related casualties in vulnerable areas.

Landslide Prediction, Satellite Imagery, Remote Sensing, Convolutional Neural Network (CNN), Artificial Intelligence (AI), Geospatial Analysis, Natural Disaster

"A Novel Approach For Prediction of Landslides Using Satellite Imagery ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 7, page no.g333-g337, July-2025, Available :http://www.jetir.org/papers/JETIR2507649.pdf

"A Novel Approach For Prediction of Landslides Using Satellite Imagery ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 7, page no. ppg333-g337, July-2025, Available at : http://www.jetir.org/papers/JETIR2507649.pdf