https://www.mdpi.com/journal/sensors/special_issues/4E670GU1OF

Geological disasters always have direct and high impacts on the development
and economic progress of countries all over the world. Landslides are a
serious natural disaster next to earthquakes and floods. It is well known that
landslides can cause human injury, loss of life, and economic devastation,
destroying construction works and causing many other damages. Thus, early
landslide detection and prediction play important roles in disaster
prevention, disaster monitoring, and several other applications. From another
perspective, a huge number of images can now be easily generated by autonomous
platforms such as UAVs or satellite sensors, which can contribute to the fast
surge in the amount of nonorganized information that may swamp data storage
facilities and help in landslide detection and risk analysis. Image analysis
and classification in the earth sciences and remote sensing has a successful
history that has now taken a huge step forward due to the capability of
computers to manage and process big data with artificial intelligence-based
approaches. In this regard, deep learning models have recently shown excellent
performance in various computer vision and digital image-related applications
such as object detection, segmentation, and classification. These
breakthroughs in deep learning and related machine learning models have also
generated tremendous interest in the computer vision and remote sensing
communities to explore deep learning for different topics, including landslide
detection and risk analysis.

This Special Issue aims to address the most up-to-date impacts of deep
learning techniques on landslide detection and geological disaster recognition
research and serves as a forum for researchers all over the world to discuss
their works and recent advancements in the field. Both theoretical studies and
state-of-the-art practical applications are welcome for submission. All the
submitted papers will be peer-reviewed and selected based on their quality and
relevance to the theme of this Special Issue.

Topics of interest include, but are not limited to:

    Hybrid algorithms using evolutionary computation, neural networks, and
fuzzy systems for landslide detection;
    Dimensionality reduction of large-scale and complex data and sparse
modeling for landslide detection applications;
    Novel deep learning approaches in the application of image/signal
processing related to landslide detection and geological disaster recognition;
    Trends in computer vision for landslide detection and geological disaster
recognition;
    Deep learning-based approaches for geological hazards analysis: data,
models, and applications;
    Landslide detection using randomization-based deep and shallow learning
techniques;
    Attention-based feature fusion in deep neural networks for detecting/
recognizing occluded objects and semantic segmentation;
    Graph convolutional networks/graph neural networks-based weakly supervised
learning approaches for landslide detection;
    Effective feature fusion in deep neural networks for detecting/recognizing
small objects;
    Deep learning for 3D scene understanding, stereo vision, decision making,
reconstruction, and object detection;
    Deep learning for landslide detection of hyperspectral remote sensing
data;
    Earthquake-triggered landslide detection from multispectral sentinel-2
imagery;
    Review remote sensing methods for landslide detection.

Dr. M. Hassaballah
Dr. Parvathaneni Naga Srinivasu
Guest Editors