Abstract Machine learning (ML)-based landslide susceptibility mapping (LSM) has achieved
substantial success in landslide risk management applications. However, the complexity of …

Complex and fragile geological conditions combined with periodic fluctuations in reservoir
water levels have led to frequent landslide disasters in the Three Gorges Reservoir area …

This paper presents a transfer learning approach to the crop classification problem based on
time series of images from the Sentinel-2 dataset labeled for two regions: Brittany (France) …

Given the time-consuming nature of compiling landslide inventories, it is increasingly
important to develop transferable landslide susceptibility models that can be applied to …

Considering the great time and labor consumption involved in conventional hazard
assessment methods in compiling landslide inventory, the construction of a transferable …

This research addresses the challenging task of predicting the stability of muddy submarine
channel slopes, crucial for ensuring safe port operations. Traditional methods falter due to …

Satellite global digital elevation models (GDEMs) suffer from positive biases in urban areas
due to building artifacts. While various machine learning (ML)-based methods have been …

Machine learning (ML) is increasingly utilized in Landslide Susceptibility Mapping (LSM),
though challenges remain in interpreting the predictions of ML models. To reveal the …

Accurate and rapid prediction of pesticides in groundwater is important to protect human
health. Thus, an electronic nose was used to recognize pesticides in groundwater. However …

Landslide susceptibility assessment is an essential tool for disaster prevention and
management. In areas with multiple fault zones, the impact of fault zone on slope stability …