Landslides are widespread occurrences that can become catastrophic when they occur near
settlements and infrastructure. Detection, monitoring and prediction are fundamental to …

Upon the introduction of machine learning (ML) and its variants, in the form that we know
today, to the landslide community, many studies have been carried out to explore the …

In the change detection (CD) task, the UNet architecture has achieved superior results.
However, due to the inherent limitation of convolution operations, UNet is inadequate in …

As natural disasters are induced by geodynamic activities or abnormal changes in the
environment, geological hazards tend to wreak havoc on the environment and human …

This study presents a new end-to-end change detection network, called difference-
enhancement dense-attention convolutional neural network (DDCNN), that is designed for …

Recent landslide detection studies have focused on pixel-based deep learning (DL)
approaches. In contrast, intuitive annotation of landslides from satellite imagery is based on …

In this paper, we do a critical review of statistical methods for landslide susceptibility
modelling and associated terrain zonations. Landslide susceptibility is the likelihood of a …

The design, implementation, management, and verification of landslide early warning
systems (LEWSs) are gaining increasing attention in the literature and among government …

This paper represents the result of the IAEG C35 Commission “Monitoring methods and
approaches in engineering geology applications” workgroup aimed to describe a general …

Map** landslides using automated methods is a challenging task, which is still largely
done using human efforts. Today, the availability of high-resolution EO data products is …