Machine Learning in Engineering Geology

Dear Colleagues,

In this Special Issue, we aim to gather high-quality original research articles, reviews, and technical notes on recent advances in the use of machine learning for engineering geology and geotechnical tasks.

With the advances in data capturing and sensor technology, and the increasing quantity and quality of multi-temporal and multi-resolution data from different platforms, data integration has become a valuable phase in many fields of geosciences. Machine learning has become the key technology to exploit such large datasets, explore unseen attributes, and identify patterns and trends that might not be apparent to human cognition, and have also made their way into the field of engineering geology.

Machine learning can already be considered a standard technique in areas such as satellite remote sensing analysis and landslide hazard modelling for larger areas, although more recently, further applications in engineering geology have emerged, such as feature detection in photos and videos, drone imagery, or three-dimensional data, e.g., for landslide or rock fall mapping and automatic rock mass classification, sensor fusion and time series analyses in geotechnical monitoring and forecasting, etc.

To explore the potential, but also the limitations, we would like to invite contributions on innovative implementations of machine learning for different tasks in engineering geology, geotechnics, and other related challenges. Original contributions, not currently under review in other journals, are solicited in relevant areas including, but not limited to, the following:

• Feature detection and object-based image classification to detect, e.g., landslides, rock fall deposits, faults, discontinuities, etc., in remotely sensed data at different scales, e.g., satellite or drone, optical images or digital elevation data, hyperspectral data, etc.;
• Point cloud classification, e.g., for rock mass characterization;
• Time series analysis/forecasting, e.g., for deformation monitoring or rainfall threshold estimation;
• Ground-breaking innovations in landslide susceptibility and hazard modelling;
• Methodological issues, such as the quality and quantity of input data and labeled data;
• Data processing and image processing.

Dr. Anika Braun
Dr. Stratis Karantanellis
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. Geosciences is an international peer-reviewed open access monthly 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 1800 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.

• machine learning
• deep learning
• remote sensing data analysis
• feature detection
• object classification
• rock mass characterization
• time series analysis
• prediction and forecast