Application of deep learning algorithms in geotechnical engineering: a short critical review
W Zhang, H Li, Y Li, H Liu, Y Chen, X Ding - Artificial Intelligence Review, 2021 - Springer
With the advent of big data era, deep learning (DL) has become an essential research
subject in the field of artificial intelligence (AI). DL algorithms are characterized with powerful …
subject in the field of artificial intelligence (AI). DL algorithms are characterized with powerful …
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 …
[HTML][HTML] Deep learning for geological hazards analysis: Data, models, applications, and opportunities
As natural disasters are induced by geodynamic activities or abnormal changes in the
environment, geological hazards tend to wreak havoc on the environment and human …
environment, geological hazards tend to wreak havoc on the environment and human …
[HTML][HTML] Evaluation of deep learning algorithms for national scale landslide susceptibility map** of Iran
The identification of landslide-prone areas is an essential step in landslide hazard
assessment and mitigation of landslide-related losses. In this study, we applied two novel …
assessment and mitigation of landslide-related losses. In this study, we applied two novel …
[HTML][HTML] Soil water erosion susceptibility assessment using deep learning algorithms
Accurate assessment of soil water erosion (SWE) susceptibility is critical for reducing land
degradation and soil loss, and for mitigating the negative impacts of erosion on ecosystem …
degradation and soil loss, and for mitigating the negative impacts of erosion on ecosystem …
Deep learning-based semantic segmentation of urban features in satellite images: A review and meta-analysis
Availability of very high-resolution remote sensing images and advancement of deep
learning methods have shifted the paradigm of image classification from pixel-based and …
learning methods have shifted the paradigm of image classification from pixel-based and …
A spatially explicit deep learning neural network model for the prediction of landslide susceptibility
With the increasing threat of recurring landslides, susceptibility maps are expected to play a
bigger role in promoting our understanding of future landslides and their magnitude. This …
bigger role in promoting our understanding of future landslides and their magnitude. This …
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 …
Machine learning-aided operations and communications of unmanned aerial vehicles: A contemporary survey
Over the past decade, Unmanned Aerial Vehicles (UAVs) have provided pervasive, efficient,
and cost-effective solutions for data collection and communications. Their excellent mobility …
and cost-effective solutions for data collection and communications. Their excellent mobility …
[HTML][HTML] Remote sensing for landslide investigations: A progress report from China
China has a significant portion of land in landslide-prone areas, and remote sensing
technologies are becoming a tool of choice to investigate and monitor landslides. Although …
technologies are becoming a tool of choice to investigate and monitor landslides. Although …