Recent Trends and advances in deep learning techniques for the classification of landslides using satellite images: comprehensive survey

Abstract

Abstract A landslide is a geographical catastrophe that occurs frequently in monsoon season and has a formidable impact over a wide range to pose risks to human lives and infrastructure worldwide. Traditional methods to classify and identify landslides are more time-consuming and less reliable. In the past few years artificial intelligence algorithms mainly, deep learning algorithms were used in many fields to detect and identify automatic disasters like landslides and earthquakes. Numerous research and classification approaches have been implemented in satellite image processing for the detection and prediction of landslides. The most challenging task in the classification and prediction of landslides from satellite imagery is to train the model with appropriate techniques and datasets which predict “accurately”. Limited work has been done on high-resolution satellite images using convolution techniques. This article presents a comprehensive study of recent deep-learning approaches based on convolutional neural networks to achieve efficient classification of landslide satellite images. A few selected research articles on deep learning approaches based on CNN for automatic detection of landside from peer reviews journals etc. are considered for this study. “The performance of all surveyed articles is evaluated using accuracy recall precision and F 1 score parameters”. This study illustrates the viability of deep learning approaches in learning complex and high-resolution satellite images for the classification and prediction of landslides.