Advances in AI-Driven Remote Sensing for Geohazard Perception
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "AI Remote Sensing".
Deadline for manuscript submissions: 28 October 2025 | Viewed by 678
Special Issue Editors
Interests: remote sensing; geohazard; computer vision; artificial intelligence
Interests: landslides; GIS; remote sensing; natural hazards
Special Issues, Collections and Topics in MDPI journals
Interests: landslide detection; landslide monitoring and early warning; InSAR
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The integration of artificial intelligence (AI) into remote sensing has revolutionized the perception of geohazards—such as landslides, earthquakes, and volcanic eruptions—that pose significant risks to human life and infrastructure. Advancements in remote sensor technologies, including optical imagery and synthetic aperture radar (SAR) imagery, have enhanced the ability to detect and analyze these geohazards. AI algorithms, particularly deep learning techniques and foundational models, have further improved the accuracy and efficiency of interpreting extensive remote sensing datasets, enabling the rapid identification of potential threats and informing disaster response strategies. This interdisciplinary approach not only enhances our cognition of geohazard features, but also contributes to more effective risk assessment and mitigation efforts, ultimately promoting resilience against geohazards.
The aim of this Special Issue is to gather interdisciplinary contributions that push the boundaries of how AI algorithms—ranging from machine learning, deep learning, and foundational models—can be harnessed to extract critical insights from remote sensing datasets. We encourage submissions that not only address technical developments and algorithmic innovations, but also discuss practical implementations and challenges encountered in real-world scenarios. Contributions may include case studies, methodological advancements, theoretical frameworks, and comparative analyses that demonstrate enhancements in the accuracy, timeliness, and reliability of geohazard detection and risk assessment.
This Special Issue invites original research and review articles that explore cutting-edge AI methodologies applied to remote sensing for geohazard perception. The scope of this Special Issue includes, but is not limited to, the following:
- AI-based algorithms for the automated feature extraction and pattern recognition of remote sensing data;
- AI-driven geohazard mapping, susceptibility mapping, and risk assessment;
- Deep learning approaches for the monitoring, prediction, and early warning of geohazard events;
- Integration of multi-sensor data (satellite, airborne LiDAR, radar, and UAV imagery) for enhanced geohazard detection, recognition, monitoring, and analysis;
- Real-time processing and decision support systems for emergency management.
Prof. Dr. Mingtao Ding
Prof. Dr. Chong Xu
Prof. Dr. Weile Li
Prof. Dr. Junchuan Yu
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- remote sensing
- artificial intelligence
- deep learning
- foundation models
- geohazard
- object detection
- object recognition
- monitoring and warning
- risk assessment
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