Landslides are one of the most critical categories of natural disasters worldwide and induce
severely destructive outcomes to human life and the overall economic system. To reduce its …

Abstract The WHO (World Health Organization) study reports that, between 1998-2017, 4.8
million people have been affected by landslides with more than 18000 deaths. The …

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 …

Recent landslide detection studies have focused on pixel-based deep learning (DL)
approaches. In contrast, intuitive annotation of landslides from satellite imagery is based on …

Landslides occur when masses of rock, earth, and other debris move down a slope. The
slope of an area is directly responsible for the magnitude of the landslide. Being able to …

Landslide identification is critical for risk assessment and mitigation. This paper proposes a
novel machine-learning and deep-learning method to identify natural-terrain landslides …

Event-based landslide inventories are essential sources to broaden our understanding of
the causal relationship between triggering events and the occurring landslides. Moreover …

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 …

Landslides are catastrophic natural hazards that often lead to loss of life, property damage,
and economic disruption. Image-based landslide investigations are crucial for determining …

Understanding the complex dynamics of global landslides is essential for disaster planners
to make timely and effective decisions that save lives and reduce the economic impacts on …