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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 1172

Special Issue Editors


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Guest Editor
School of Geology Engineering and Geomatics, Chang’an University, No.126 Yanta Road, Xi’an 710054, China
Interests: remote sensing; geohazard; computer vision; artificial intelligence

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Guest Editor
State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China
Interests: landslide detection; landslide monitoring and early warning; InSAR
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China
Interests: remote sensing; deep learning; geohazard; hyperspectral; visual foundation model

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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Published Papers (1 paper)

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Research

27 pages, 17902 KB  
Article
Identification of Dominant Controlling Factors and Susceptibility Assessment of Coseismic Landslides Triggered by the 2022 Luding Earthquake
by Jin Wang, Mingdong Zang, Jianbing Peng, Chong Xu, Zhandong Su, Tianhao Liu and Menghao Li
Remote Sens. 2025, 17(16), 2797; https://doi.org/10.3390/rs17162797 - 12 Aug 2025
Viewed by 314
Abstract
Coseismic landslides are geological events in which slopes, either on the verge of instability or already in a fragile state, experience premature failure due to seismic shaking. On 5 September 2022, an Ms 6.8 earthquake struck Luding County, Sichuan Province, China, triggering numerous [...] Read more.
Coseismic landslides are geological events in which slopes, either on the verge of instability or already in a fragile state, experience premature failure due to seismic shaking. On 5 September 2022, an Ms 6.8 earthquake struck Luding County, Sichuan Province, China, triggering numerous landslides that caused severe casualties and property damage. This study systematically interprets 13,717 coseismic landslides in the Luding earthquake’s epicentral area, analyzing their spatial distribution concerning various factors, including elevation, slope gradient, slope aspect, plan curvature, profile curvature, surface cutting degree, topographic relief, elevation coefficient variation, lithology, distance to faults, epicentral distance, peak ground acceleration (PGA), distance to rivers, fractional vegetation cover (FVC), and distance to roads. The analytic hierarchy process (AHP) was improved by incorporating frequency ratio (FR) to address the subjectivity inherent in expert scoring for factor weighting. The improved AHP, combined with the Pearson correlation analysis, was used to identify the dominant controlling factor and assess the landslide susceptibility. The accuracy of the model was verified using the area under the receiver operating characteristic (ROC) curve (AUC). The results reveal that 34% of the study area falls into very-high- and high-susceptibility zones, primarily along the Moxi segment of the Xianshuihe fault and both sides of the Dadu river valley. Tianwan, Caoke, Detuo, and Moxi are at particularly high risk of coseismic landslides. The elevation coefficient variation, slope aspect, and slope gradient are identified as the dominant controlling factors for landslide development. The reliability of the proposed model was evaluated by calculating the AUC, yielding a value of 0.8445, demonstrating high reliability. This study advances coseismic landslide susceptibility assessment and provides scientific support for post-earthquake reconstruction in Luding. Beyond academic insight, the findings offer practical guidance for delineating priority zones for risk mitigation, planning targeted engineering interventions, and establishing early warning and monitoring strategies to reduce the potential impacts of future seismic events. Full article
(This article belongs to the Special Issue Advances in AI-Driven Remote Sensing for Geohazard Perception)
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