Machine Learning and Remote/Proximal Sensing for Rock Mass Characterization and Slope Analyses
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: 30 November 2024 | Viewed by 72
Special Issue Editors
Interests: Geomechanics; engineering geology; rock mass characterization; applied hydrogeology; geomatics applied to engineering geology; machine learning
Interests: machine learning; mobile robotics (UAV, UGV, USV); remote sensing; hyperspectral image analysis; precision farming; geographical information systems (GIS)
Special Issues, Collections and Topics in MDPI journals
Interests: engineering geology; remote sensing; natural hazards; landslides; numerical modelling
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Geomechanical studies are crucial for understanding the behavior of rock masses in various engineering applications. The advent of machine learning (ML) and the recent innovation of proximal and remote sensing (RS) techniques have remarkably changed our approach to enhance the characterization of rock masses. This Special Issue aims to explore the use of ML and RS and their potential synergy in advancing geomechanical analyses for improved infrastructure design and hazard mitigation.
This Special Issue seeks to showcase innovative research at the intersection of ML and RS for rock mass characterization in geomechanics. It aims to elucidate the potential of using and integrating these technologies to extract valuable information from diverse data sources and improve our understanding of rock mass behavior. Research papers that explore machine learning techniques for integrating hydrological and mechanical data to better understand the coupled behavior of water flow and mechanical responses in rock masses are welcomed.
We invite contributions addressing, but not limited to, the following topics:
- The development of ML algorithms for analyzing remote sensing data (e.g., satellite imagery, LiDAR, Terrestrial Laser Scanner, UAV-based imagery) to characterize rock mass properties.
- The integration of multi-source data, including geological surveys, ground-based monitoring, and RS imagery, for comprehensive rock mass characterization.
- The application of ML techniques for feature extraction, classification, and change detection in RS data to assess rock mass stability and deformation.
- The fusion of ML and RS for the real-time monitoring and predictive modeling of rock mass behavior in response to external factors, such as weathering, seismic activity, and human activities.
- Case studies demonstrating the practical utility of ML and RS integration in geomechanical engineering projects, such as slope stability analysis, tunneling, and underground excavations.
- The hydro-mechanical characterization of rock masses.
This Special Issue aims to foster interdisciplinary collaboration and innovation by bringing together researchers, engineers, and practitioners from the fields of geomechanics, remote sensing, and machine learning. By leveraging the complementary strengths of ML and RS, we can unlock new insights into rock mass behavior and improve the resilience and sustainability of civil engineering projects.
Dr. Elisa Mammoliti
Dr. Adriano Mancini
Dr. Mirko Francioni
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
- geomechanics and proximal/remote sensing
- machine learning
- rock mass characterization
- discontinuities analysis
- fracturing
