Landslide hazards have significant social, economic, and environmental impact. This work
provides a critical review of the main existing literature using satellite data for map** …

While semantic segmentation has seen tremendous improvements in the past, there are still
significant labeling efforts necessary and the problem of limited generalization to classes …

Multiple landslide events occur often across the world which have the potential to cause
significant harm to both human life and property. Although a substantial amount of research …

In this work, we present the CAS Landslide Dataset, a large-scale and multisensor dataset
for deep learning-based landslide detection, developed by the Artificial Intelligence Group at …

Rapid and accurate map** of landslides triggered by extreme events is essential for
effective emergency response, hazard mitigation, and disaster management. However, the …

Landslide detection is crucial for disaster management and prevention. With the advent of
multi-channel optical remote sensing technology, detecting landslides have become more …

Landslides frequently cause serious property damage and casualties. Therefore, it is crucial
to have rapid and accurate landslide map** (LM) to support post-earthquake landslide …

Efficient and automatic landslide detection solutions are beneficial for regional hazard
mitigation. At present, scholars have carried out landslide detection based on deep learning …

Landslides are major natural disasters in mountainous areas, often caused by earthquakes
and heavy rainfalls. Traditional manual delineation methods for identifying landslide …

The production of long-term landslide maps (LAM) holds crucial importance in estimating
landslide activity, vegetation disturbance, and regional stability. However, the availability of …