Landslides represent one of the world's most dangerous and widespread risks, annually
causing thousands of deaths and billions of dollars worth of damage. Building on and …

The main objective of this study is to evaluate and compare the performance of different
machine learning (ML) algorithms, namely, Artificial Neural Network (ANN), Extreme …

This paper systematically reviews remote sensing technology and learning algorithms in
exploring landslides. The work is categorized into four key components:(1) literature search …

Landslides are major geological hazards and frequently occur in mountainous areas with
steep slopes, often causing significant loss. Interferometric Synthetic Aperture Radar …

Landslide is one of the most dangerous and frequently occurred natural disasters. The
semantic segmentation technique is efficient for wide area landslide identification from high …

Landslides are still wreaking havoc in many parts of the world. Several previous landslide
susceptibility map** (LSM) studies have proven that fuzzy logic methods provide reliable …

Landslide detection map** (LDM) is the basis of the field of landslide disaster prevention;
however, it has faced certain difficulties. The Three Gorges Reservoir area of the Yangtze …

Landslide detection is a hot topic in the remote sensing community, particularly with the
current rapid growth in volume (and variety) of Earth observation data and the substantial …

This paper proposes a new approach based on an unsupervised deep learning (DL) model
for landslide detection. Recently, supervised DL models using convolutional neural …

Landslides are a natural hazard that can endanger human life and cause severe
environmental damage. A landslide susceptibility map is essential for planning, managing …