Landslides are geomorphological processes that shape the landscapes of all continents,
dismantling mountains and contributing sediments to the river networks. Caused by …

In recent decades, data-driven landslide susceptibility models (DdLSM), which are based on
statistical or machine learning approaches, have become popular to estimate the relative …

Landslides recurrently impact the Italian territory, producing huge economic losses and
casualties. Because of this, there is a large demand for monitoring tools to support landslide …

This paper describes a novel methodology where Machine Learning Algorithms (MLAs)
have been integrated to assess the landslide risk for slow moving mass movements …

Landslide databases are a potential tool for the analysis of landslide susceptibility, hazard,
and risk. Additionally, the spatio-temporal distribution of landslides and their correlation with …

The rising availability of satellite-based multi-temporal interferometric datasets covering
large areas of the Earth surface constitutes a huge asset in the context of operational …

Cracks in the crown area are one of the important indicators in the instability of the landslide
headscarp, potentially offering early warnings for landslide hazards. Due to the complex …

Landslide monitoring is a mandatory step in landslide risk assessment. It requires collecting
data on landslide conditions (eg, areal extent, landslide kinematics, surface topography …

Italy is frequently hit and damaged by landslides, resulting in substantial and widespread
disruptions. In particular, slope failures have a high impact on the population …

Detailed information about landslide occurrence is the foundation for advancing process
understanding, susceptibility mapping, and risk reduction. Despite the recent revolution in …