Special Issue "Applications of Machine Learning on Earth Sciences"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences and Geography".

Deadline for manuscript submissions: 30 September 2020.

Special Issue Editors

Dr. Luciano Zuccarello
Guest Editor
Universidad de Granada, Granada, Spain
Interests: Geophysics, Seismology, Volcanology, Artificial Intelligence
Dr. Janire Prudencio
Guest Editor
Department of Theoretical Physics and Cosmic - Physical Area of ​​the Earth, University of Granada, Campus of Fuentenueva, Granada, E-18071, Spain
Interests: Seismology; Seismics; Scattering; Inversion; Geology-Volcanology; Tectonics; Earthquake Seismology

Special Issue Information

Dear Colleagues,

We are pleased to inform you that Applied Sciences is currently running a Special Issue entitled "Applications of Machine Learning on Earth Sciences".

In recent years, interest has increased in time series and image processing analyses in a number of fields related to earth sciences. The improvements in data acquisition systems have increased the quantity and quality of data analysed, processed, and interpreted, and have shortened the time in which results can be produced. The large data volume acquired by the different acquisition systems requires suitable analysis tools that enhance traditional approaches by extracting and applying the latent knowledge embedded in the data. One of the key challenges is structuring and organising the huge amount of raw data; the type of information that could aid the scientific community must be determined to achieve a deeper knowledge of the complex dynamics that govern the geophysical and geochemical systems of our planet. Upcoming methodologies need to address the long-term challenges of data management and accessibility. Data mining, cloud computing, and machine learning are the most appropriate disciplines for the analyses of these high throughput data.

In this Special Issue, we welcome contributions concerning recent machine learning advances applied to earth sciences that improve our understanding of the complexity of our planet. We would also appreciate if you could forward this to your team members and colleagues who may also be interested in the topic. We look forward to hearing from you, and we remain at your disposal for more information.

Dr. Luciano Zuccarello
Dr. Janire Prudencio
Guest Editor

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 papers will be 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. Applied Sciences 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 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
  • geophysics
  • geochemistry
  • remote sensing
  • seismology
  • volcanology
  • satellite observations
  • artificial intelligence
  • data mining
  • cloud computing

Published Papers (1 paper)

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Open AccessArticle
UVI Image Segmentation of Auroral Oval: Dual Level Set and Convolutional Neural Network Based Approach
Appl. Sci. 2020, 10(7), 2590; https://doi.org/10.3390/app10072590 - 09 Apr 2020
The auroral ovals around the Earth’s magnetic poles are produced by the collisions between energetic particles precipitating from solar wind and atoms or molecules in the upper atmosphere. The morphology of auroral oval acts as an important mirror reflecting the solar wind-magnetosphere-ionosphere coupling [...] Read more.
The auroral ovals around the Earth’s magnetic poles are produced by the collisions between energetic particles precipitating from solar wind and atoms or molecules in the upper atmosphere. The morphology of auroral oval acts as an important mirror reflecting the solar wind-magnetosphere-ionosphere coupling process and its intrinsic mechanism. However, the classical level set based segmentation methods often fail to extract an accurate auroral oval from the ultraviolet imager (UVI) image with intensity inhomogeneity. The existing methods designed specifically for auroral oval extraction are extremely sensitive to the contour initializations. In this paper, a novel deep feature-based adaptive level set model (DFALS) is proposed to tackle these issues. First, we extract the deep feature from the UVI image with the newly designed convolutional neural network (CNN). Second, with the deep feature, the global energy term and the adaptive time-step are constructed and incorporated into the local information based dual level set auroral oval segmentation method (LIDLSM). Third, we extract the contour of the auroral oval through the minimization of the proposed energy functional. The experiments on the UVI image data set validate the strong robustness of DFALS to different contour initializations. In addition, with the help of deep feature-based global energy term, the proposed method also obtains higher segmentation accuracy in comparison with the state-of-the-art level set based methods. Full article
(This article belongs to the Special Issue Applications of Machine Learning on Earth Sciences)
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