Deep Learning in Landslide Studies: A Review

Abstract

AbstractThis study reviews deep learning architectures and techniques used in the landslide domain. This study aims to understand the state of the art, challenges, and opportunities of applying deep learning to landslide research. Every paper discussed in this article is reviewed for the deep learning approach employed, the study area where it is implemented, additional benchmark algorithms implemented, model assessment metrics, the best model that is selected, and the limitations mentioned by the authors. This review increases visibility into (1) various deep learning methodologies as implemented in real-world landslide mapping, detection, monitoring, and prediction case studies, (2) projects constraints of applying deep learning to landslide research (3) provides recommendations and breakthroughs that must be established in certain areas of landslide studies.