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Geohazard Monitoring Based on Remote Sensing and Artificial Intelligence

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: 31 August 2026 | Viewed by 845

Special Issue Editor


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Guest Editor
Department of Geodesy and Geoinformatics, Faculty of Geoengineering, Mining and Geology, Wroclaw University of Science and Technology, ul. Na Grobli 15, L-1 Geocentrum, 50-421 Wrocław, Poland
Interests: remote sensing; planetary geology; ore geology; GIS; geomorphology

Special Issue Information

Dear Colleagues,

Geohazards—including landslides, earthquakes, volcanic activity, floods, droughts, wildfires, and coastal erosion—pose increasing threats to human life, critical infrastructure, and natural ecosystems under changing climatic and anthropogenic conditions. Recent advances in satellite-based remote sensing and artificial intelligence (AI) enable comprehensive, large-scale, and near-real-time monitoring of these hazards. This Special Issue focuses on innovative methodologies for geohazard monitoring, assessment, and early warning through the integration of multi-source remote sensing data and AI-driven approaches.

We invite original research articles and review papers employing multi-sensor satellite observations, including optical, thermal, and radar data from the Sentinel and Landsat missions, Synthetic Aperture Radar (SAR) and InSAR time series, as well as complementary LiDAR, UAV, and GNSS measurements. Contributions may address machine learning and deep learning techniques for automated hazard detection, deformation monitoring, change detection, susceptibility mapping, and predictive modelling.

Additional topics of interest include data fusion, spatio-temporal analysis, uncertainty quantification, and the development of operational monitoring frameworks. Case studies at local, regional, and global scales demonstrating practical applications for disaster risk reduction, climate adaptation, and sustainable land management are strongly encouraged. By fostering interdisciplinary research at the interface of remote sensing, geosciences, and artificial intelligence, this Special Issue aims to advance robust and transferable solutions for geohazard monitoring and informed decision-making.

Dr. Marta Ciążela
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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

  • geohazard monitoring
  • remote sensing
  • GIS
  • artificial intelligence
  • climate-related hazards
  • data fusion

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Published Papers (1 paper)

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Research

22 pages, 6838 KB  
Article
A Dynamic Landslide Susceptibility Assessment Method Based on Multi-Source Remote Sensing, XGBoost, and SHAP: A Case Study in Yongsheng County, Yunnan Province
by Shuhao Yan, Shanshan Wang, Yixuan Guo, Xingxing Rong, Dan Zhao and Wei Li
Remote Sens. 2026, 18(6), 845; https://doi.org/10.3390/rs18060845 - 10 Mar 2026
Viewed by 623
Abstract
Landslide susceptibility assessment (LSA) heavily depends on the completeness of landslide inventories and the interpretability of predictive models. Conventional inventories, based solely on historical records, often fail to identify newly occurring or slow-moving landslides, leading to biased susceptibility estimates. To address this limitation, [...] Read more.
Landslide susceptibility assessment (LSA) heavily depends on the completeness of landslide inventories and the interpretability of predictive models. Conventional inventories, based solely on historical records, often fail to identify newly occurring or slow-moving landslides, leading to biased susceptibility estimates. To address this limitation, this study proposes a dynamic LSA framework that integrates multi-source remote sensing data, Extreme Gradient Boosting (XGBoost) modeling, and Shapley Additive Explanations (SHAP), with a case study in Yongsheng County, Yunnan Province, China. This study jointly uses multi-temporal optical remote sensing imagery and Sentinel-1 InSAR (Interferometric Synthetic Aperture Radar) deformation data to update the landslide inventory. Compared with the historical inventory containing 334 landslide points, the updated inventory incorporates an additional 140 deformation-related landslide hazard points. XGBoost models were developed using conditioning factors selected through multicollinearity analysis to evaluate the influence of inventory completeness on model performance. Results show that the model based on the updated inventory achieves a significant improvement in predictive accuracy. SHAP-based interpretation reveals that distance to roads and maximum deformation rate are the dominant factors controlling landslide occurrence, reflecting the combined effects of human activities and dynamic ground deformation. The resulting susceptibility map shows that the Area Under the Curve (AUC) value for susceptibility zoning of the updated sample increases from 0.857 to 0.928, with high and very high susceptibility zones occupying 8.28% of the study area. Overall, the proposed framework improves both the accuracy and interpretability of LSA and demonstrates the effectiveness of multi-source remote sensing data for dynamic landslide hazard assessment in mountainous regions. Full article
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