Machine Learning and Computer Vision Techniques in Geosciences: Laboratory and Field Applications
A special issue of Minerals (ISSN 2075-163X). This special issue belongs to the section "Crystallography and Physical Chemistry of Minerals & Nanominerals".
Deadline for manuscript submissions: closed (13 May 2022) | Viewed by 3918
Special Issue Editors
Interests: rock mechanics; mining; machine-learning techniques; image processing; petrography; arduino/raspberry Pi in geosciences
Special Issues, Collections and Topics in MDPI journals
Interests: machine learning techniques: applications and new algorithms; functional statistics: outliers detection and quality control; image processing;
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
We kindly invite you to contribute to a new Special Issue for Minerals, which will be focused on applications of machine learning and computer vision in geosciences, with particular dedication to the fields of mineralogy and petrology.
Mineralogy and petrology stand as the basis of geosciences and are fundamental to a proper understanding of the formation, development, and exploitation of mineral deposits, as well as oil, gas, and geothermal resources. Features such as texture, internal structure, mechanical properties, and composition may affect, at different scales, the behavior of minerals and rocks, which are essential resources in building and manufacturing processes.
The ability to recognize structures and patterns at different scales is fundamental, not only for a correct identification of minerals and rocks, but also for the determination of parameters that influence the exploitation of natural resources, such as porosity or granulometry. Even though the role of experts may never be replaced, it is true that there are many techniques developed so far that have helped and optimized classically manual processes in geosciences, such as mineral and rock identification through thin-section microscopy or rock-mass structure recognition. These advances are backboned by machine learning and computer vision techniques.
Moreover, the availability of 3D resources has proved to be very useful in the field of education in geosciences, where access to real-life environments is not always easy due to time and cost limitations. The combination of computer vision techniques with virtual reality allows the possibility of having available 3D virtual replicas of spaces and materials (like mineral and rock specimens) that represent an interesting potential for educational purposes.
In this Special Issue, we invite researchers to contribute with developments related to the application of machine learning and computer vision techniques in the field of geosciences, with particular emphasis on mineralogy and petrology.
Dr. Ignacio Pérez-Rey
Prof. Dr. Javier Martínez
Dr. Mauro Muñiz-Menéndez
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. Minerals 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 2400 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
- mineralogy
- petrology
- petrography
- machine learning
- computer vision
- mining engineering
- 3D point cloud
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