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 …

Landslide susceptibility assessment is essential for regional landslide risk assessment and
mitigation. Most past studies involved cell-based analysis that takes landslide incidents as …

In recent years, landslide susceptibility mapping has substantially improved with advances
in machine learning. However, there are still challenges remain in landslide mapping due to …

Due to the similarity of conditioning factors, the aggregation feature of landslides and the
multi-temporal landslide inventory, the spatial and temporal effects of landslides need to be …

Shallow slope failures triggered by rainfall commonly pose considerable risks in
mountainous areas. In order to delineate areas where landslides are more prone to occur …

This paper focuses on landslide susceptibility prediction in Nanchuan, a high-risk landslide
disaster area. The evidential belief function (EBF)-based function tree (FT), logistic …

The Himalayan range is one of the most tectonically active mountain ranges on Earth. The
Jilong Valley is a deeply-dissected canyon in the Chinese Himalayas in Tibet, and in this …

Landslide susceptibility assessment is often hindered by the lack of historical landslide
records. In this study, we propose a transfer learning-based approach for landslide …

Assessing and mapping landslide susceptibility is a powerful approach to decrease the cost
of landslide disasters. The aim of this paper is to design combination models by combining …

Landslide debris will travel certain distances and threaten people and properties along its
runout path, highlighting the importance of runout path prediction in landslide risk …