We conducted a systematic literature review of 105 landslide susceptibility studies in Italy
from 1980 to 2023, retrieved from the Scopus database. We discovered that Italian …

This study evaluates deep learning (DL) models, particularly ResU-Net with attention
mechanisms, for map** landslides in Quebec, Canada, utilizing high-resolution digital …

Transformer-based semantic segmentation architectures excel in extracting road networks
from very-high-resolution (VHR) satellite images due to their ability to capture global …

Permafrost destabilization induced hazard-susceptibility modeling was performed using the
machine-learning models (Random Forest and Logistic Regression) in the Alaknanda basin …

In this study, landslide susceptibility maps (LSMs) were produced for three regions where
landslides are common in the Eastern Black Sea Region of Türkiye. The regions studied …

Landslides are major geological hazards globally, causing significant economic losses each
year. Accurate landslide detection is essential for disaster prevention, risk assessment, and …

Flood disasters are frequent, sudden, and have significant chain effects, seriously damaging
infrastructure. Remote sensing images provide a means for timely flood emergency …

This study proposes a model for creating facial wound-covering masks to support patients
recovering from injuries, especially those with scars or deformities resulting from accidents …

In the field of remote sensing (RS) image semantic segmentation, existing supervised
learning methods rely on a large amount of labeled data, which limits their application …

Transformer-based semantic segmentation architectures excel in extracting road networks
from very-highresolution (VHR) satellite images due to their ability to capture global …