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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: 15 June 2025 | Viewed by 2220

Special Issue Editors


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Guest Editor
Dipartimento di Scienze e Ingegneria della Materia, dell’Ambiente ed Urbanistica, Università Politecnica delle Marche, via Brecce Bianche 12, 60131 Ancona, Italy
Interests: Geomechanics; engineering geology; rock mass characterization; applied hydrogeology; geomatics applied to engineering geology; machine learning

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Guest Editor
Dipartimento di Ingegneria dell ’Informazione —DII, Università Politecnica delle Marche, 60131 Ancona, Italy
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

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:

  1. 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.
  2. The integration of multi-source data, including geological surveys, ground-based monitoring, and RS imagery, for comprehensive rock mass characterization.
  3. The application of ML techniques for feature extraction, classification, and change detection in RS data to assess rock mass stability and deformation.
  4. 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.
  5. Case studies demonstrating the practical utility of ML and RS integration in geomechanical engineering projects, such as slope stability analysis, tunneling, and underground excavations.
  6. 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

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Published Papers (1 paper)

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Research

27 pages, 17955 KiB  
Article
Characterization of Complex Rock Mass Discontinuities from LiDAR Point Clouds
by Yanan Liu, Weihua Hua, Qihao Chen and Xiuguo Liu
Remote Sens. 2024, 16(17), 3291; https://doi.org/10.3390/rs16173291 - 4 Sep 2024
Cited by 1 | Viewed by 1496
Abstract
The distribution and development of rock mass discontinuities in 3D space control the deformation and failure characteristics of the rock mass, which in turn affect the strength, permeability, and stability of rock masses. Therefore, it is essential to accurately and efficiently characterize these [...] Read more.
The distribution and development of rock mass discontinuities in 3D space control the deformation and failure characteristics of the rock mass, which in turn affect the strength, permeability, and stability of rock masses. Therefore, it is essential to accurately and efficiently characterize these discontinuities. Light Detection and Ranging (LiDAR) now allows for fast and precise 3D data collection, which supports the creation of new methods for characterizing rock mass discontinuities. However, uneven density distribution and local surface undulations can limit the accuracy of discontinuity characterization. To address this, we propose a method for characterizing complex rock mass discontinuities based on laser point cloud data. This method is capable of processing datasets with varying densities and can reduce over-segmentation in non-planar areas. The suggested approach involves a five-stage process that includes: (1) adaptive resampling of point cloud data based on density comparison; (2) normal vector calculation using Principal Component Analysis (PCA); (3) identifying non-planar areas using a watershed-like algorithm, and determine the main discontinuity sets using Multi-threshold Mean Shift (MTMS); (4) identify single discontinuity clusters using Density-Based Spatial Clustering of Applications with Noise (DBSCAN); (5) fitting discontinuity planes with Random Sample Consensus (RANSAC) and determining discontinuity orientations using analytic geometry. This method was applied to three rock slope datasets and compared with previous research results and manual measurement results. The results indicate that this method can effectively reduce over-segmentation and the characterization results have high accuracy. Full article
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