Recent advances in the diversity and systematization of design methods and real-time data
have led to a general elevation in spatio-temporal accuracy for regional landslide early …

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 …

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 …

The selection of non-landslide samples has a great impact on the machine learning
modelling for landslide susceptibility prediction (LSP). This study presents a novel …

For decades, the distinction between statistical models and machine learning ones has
been clear. The former are optimized to produce interpretable results, whereas the latter …

Most epistemic uncertainty within data-driven landslide susceptibility assessment results
from errors in landslide inventories, difficulty in identifying and map** landslide causes …

The determination of map** units, including grid, slope, unique condition, administrative
division, and watershed units, is a very important modeling basis for landslide assessments …