Machine learning and landslide studies: recent advances and applications
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 …
today, to the landslide community, many studies have been carried out to explore the …
Review of landslide susceptibility assessment based on knowledge mapping
C Yong, D Jinlong, G Fei, T Bin, Z Tao, F Hao… - … Research and Risk …, 2022 - Springer
Landslide susceptibility assessment is highly valuable for disaster prevention and mitigation.
This study utilized the aspects of data and content to comprehensively examine the research …
This study utilized the aspects of data and content to comprehensively examine the research …
Landslide detection using deep learning and object-based image analysis
O Ghorbanzadeh, H Shahabi, A Crivellari… - Landslides, 2022 - Springer
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 …
approaches. In contrast, intuitive annotation of landslides from satellite imagery is based on …
Detection and segmentation of loess landslides via satellite images: A two-phase framework
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 …
and economic disruption. Image-based landslide investigations are crucial for determining …
Landslide4sense: Reference benchmark data and deep learning models for landslide detection
This study introduces\textit {Landslide4Sense}, a reference benchmark for landslide
detection from remote sensing. The repository features 3,799 image patches fusing optical …
detection from remote sensing. The repository features 3,799 image patches fusing optical …
Landslide detection in the Himalayas using machine learning algorithms and U-Net
Event-based landslide inventories are essential sources to broaden our understanding of
the causal relationship between triggering events and the occurring landslides. Moreover …
the causal relationship between triggering events and the occurring landslides. Moreover …
A comprehensive transferability evaluation of U-Net and ResU-Net for landslide detection from Sentinel-2 data (case study areas from Taiwan, China, and Japan)
Earthquakes and heavy rainfalls are the two leading causes of landslides around the world.
Since they often occur across large areas, landslide detection requires rapid and reliable …
Since they often occur across large areas, landslide detection requires rapid and reliable …
Recognition and mapping of landslide using a fully convolutional DenseNet and influencing factors
The recognition and mapping of landslide (RML) is an important task in hazard and risk
research and can provide a scientific basis for the prevention and control of landslide …
research and can provide a scientific basis for the prevention and control of landslide …
The application of ResU-net and OBIA for landslide detection from multi-temporal sentinel-2 images
Landslide detection is a hot topic in the remote sensing community, particularly with the
current rapid growth in volume (and variety) of Earth observation data and the substantial …
current rapid growth in volume (and variety) of Earth observation data and the substantial …
A dual-encoder U-Net for landslide detection using Sentinel-2 and DEM data
W Lu, Y Hu, Z Zhang, W Cao - Landslides, 2023 - Springer
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 …
term land use planning. Deep learning–based methods represented by convolutional neural …