Artificial intelligence (AI) holds significant promise for advancing natural disaster
management through the use of predictive models that analyze extensive datasets, identify …

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 …

Earthquake-triggered landslides frequently occur in active mountain areas, which poses
great threats to the safety of human lives and public infrastructures. Fast and accurate …

The effects of landslides have been exponentially increasing due to the rapid growth of
urbanization and global climate change. The information gained from predictive models and …

Efficient landslide mapping from high spatial resolution images is important in many
practical applications, such as emergency response. Numerous studies and methods have …

Accurate landslide extraction is significant for landslide disaster prevention and control.
Remote sensing images have been widely used in landslide investigation, and landslide …

Semantic segmentation is a crucial task in remote sensing and has been predominantly
performed using convolutional neural networks (CNNs) for the past decade. Recently …

In recent years, remote sensing technologies have played a crucial role in the detection and
management of natural disasters. In this context, deep learning models are of great …

Rapid and accurate identification of landslides is an essential part of landslide hazard
assessment, and in particular it is useful for land use planning, disaster prevention, and risk …

Accurate and timely landslide mapping plays a critical role in emergency response and long-
term land use planning. Deep learning–based methods represented by convolutional neural …