Landslide Hazard Prediction using Satellite Imagery and DL Approaches

Abstract: Artificial intelligence (AI) techniques for landslide prediction in hilly areas, where natural and man-made factors can cause significant damage. While machine learning and feature-based classification methods have been applied to satellite imagery for landslide detection, achieving fully automatic and accurate predictions remains challenging due to data limitations and model performance. This study reviews 50 papers on machine and deep learning algorithms, highlighting research gaps and proposing a novel approach using a modified ResNet101 deep learning model. The proposed model achieves 100% accuracy on an augmented Beijing dataset of more than 4000 satellite images. The findings offer insights into the latest techniques, research gaps, and potentialadvancements in landslide classification using satellite images, providing a resource for researchers and encouraging innovation in this domain.