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 …

Due to limited resources and environmental pollution, monitoring the geological
environment has become essential for many countries' sustainable development. As various …

Large earthquakes initiate chains of surface processes that last much longer than the brief
moments of strong shaking. Most moderate‐and large‐magnitude earthquakes trigger …

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

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 …

Develo** solar photovoltaic (PV) systems is an effective way to address the problems of
limited fossil fuel reserves, soaring world energy demand and global climate change. The …

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 …

Data-driven landslide susceptibility models formally integrate spatial landslide information
with explanatory environmental variables that describe predisposing factors of slope …

Correct use of multi-temporal Interferometric Synthetic Aperture Radar (InSAR) datasets to
complement geodetic surveying for geo-hazard applications requires rigorous assessment …