Next-Generation AI, Advanced Numerical Modeling and Multi-Sensor Fusion for Landslide Intelligence
A Special Issue of Remote Sensing (ISSN 2072-4292) belonging to the section "Remote Sensing in Geology, Geomorphology and Hydrology".
Deadline for manuscript submissions: 31 March 2027 | Viewed by 47
Editors
Interests: geotechnics; earthquake; earthquake engineering; geotechnical engineering; remote sensing
Interests: natural hazard; slope stability; numerical modelling; geomechanics; rock mechanics; geodesy and surveying; geoinformatics (GIS); remote sensing
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Special Issue Information
Dear Colleagues,
Landslides are among the most significant natural hazards, posing serious threats to the environment, critical infrastructure, and public safety. The increasing availability of data acquired through different Earth Observation techniques has opened new opportunities for investigating and managing landslide risk. However, the large volume and redundancy of information generated by these technologies often make data integration and interpretation particularly challenging. In recent years, advances in artificial intelligence (AI) have significantly enhanced the ability to monitor, analyse, and predict landslide processes. In this context, AI-based methodologies have emerged as powerful tools, enabling the extraction of important information from large and heterogeneous datasets while improving the understanding, characterization, and forecasting of landslide phenomena.
This Special Issue brings together contributions from researchers and practitioners focused on the development and application of innovative AI-based approaches for the integration of data derived from multiple Earth Observation techniques, including satellite imagery, Unmanned Aerial Vehicle (UAV) systems, LiDAR surveys, and in situ sensor networks, combined with advanced machine learning methods and numerical modelling.
Special attention is given to the prediction of ground movements and the detection of ground deformations as potential precursors of larger landslide events. The Special Issue aims to promote the development of reliable, scalable, and operationally applicable models to support continuous monitoring, multitemporal analysis, simulation of landslide evolution and landslide process forecasting. The advances are expected to contribute ultimately to better landslide risk assessment, management and mitigation strategies and will lead to more effective and resilient hazard management.
Contributions may include, but are not limited to, the following topics:
- Machine learning and deep learning techniques for landslide detection and classification;
- Advanced multi-sensor methods for the integration of heterogeneous datasets;
- Applications of InSAR, GNSS, LiDAR, and UAV data for landslide investigation and monitoring;
- Advanced numerical modelling of landslide processes;
- Multitemporal analysis and continuous landslide monitoring;
- AI-based early warning systems for landslide hazard;
- Geospatial big data analytics and cloud-based processing platforms;
- Integration of Internet of Things (IoT) technologies and edge computing for real-time landslide monitoring;
- Predictive modelling and assessment of landslide susceptibility and hazard.
Dr. Massimo Mangifesta
Dr. Mirko Francioni
Dr. Mariagiulia Annibali Corona
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 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-anonymized 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
- multi-sensor data fusion
- artificial intelligence
- machine learning
- numerical modelling
- geospatial big data
- early warning systems
- landslide susceptibility
- IoT
- edge computing
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