Knowledge about historic landslide event occurrence is important for supporting disaster risk
reduction strategies. Building upon findings from 2022 Landslide4Sense Competition, we …

This study introduces\textit {Landslide4Sense}, a reference benchmark for landslide
detection from remote sensing. The repository features 3,799 image patches fusing optical …

This study introduces Landslide4Sense, a reference benchmark for landslide detection from
remote sensing. The repository features 3,799 image patches fusing optical layers from …

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

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 …

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 …

Accurate landslide detection is essential for disaster mitigation and relief. In this study, we
develop a feature enhancement framework that integrates attention and multiscale …

Landslide detection based on remote sensing images is an effective method for rapidly and
accurately detecting landslide regions, which can aid in disaster prevention and mitigation …

Landslide detection from high-resolution satellite imagery plays a significant role in disaster
management. Recently, deep learning has emerged as one of the most powerful tools for …

Deep learning (DL) models have been widely used for remote sensing-based landslide
mapping due to their impressive capabilities for automatic information extraction. However …