Journal Information
Computers & Geosciences
https://www.sciencedirect.com/journal/computers-and-geosciences
Impact Factor:
2.721
Publisher:
Elsevier
ISSN:
0098-3004
Viewed:
656
Tracked:
0

Call For Papers
Computers & Geosciences publishes high impact, original research at the interface between Computer Sciences and Geosciences. Publications should apply modern computer science paradigms, whether computational or informatics-based, to address problems in the geosciences.

Computational/informatics elements may include: computational methods; algorithms; data models; database retrieval; information retrieval; near and remote sensing data analysis; data processing; artificial intelligence; computer graphics; computer visualization; programming languages; parallel systems; distributed systems; the World-Wide Web; social media; ontologies; and software engineering.

Geoscientific topics of interest include: mineralogy; petrology; geochemistry; geomorphology; paleontology; stratigraphy; structural geology; sedimentology; hydrology; hydrogeology; oceanography; atmospheric sciences; climatology; meteorology; geophysics; geomatics; seismology; geodesy; paleogeography; environmental science; soil science; glaciology.

Other fields may be considered but are not regarded as a priority.

Computers & Geosciences does not consider:

    Geoscience manuscripts that do not contain a significant computer science innovation. Pure methodological developments (e.g. geophysics, hydrology) are not considered. Pure analytical developments are not considered, unless they have significant implications on computational geoscientific problems.
    Computer science manuscripts with no clear application to the geosciences (as defined above).
    Manuscripts aiming at solving a geoscientific engineering problem rather than answering a scientific question
    Standard code of already well-established, or previously published methods
    Graphical User Interfaces (GUIs), unless they provide an original solution to a non-trivial input-handling problem.
    Manuscripts that use GIS tools in standard ways

Code and Data: Computers & Geosciences aims to publish code and supporting data from accepted manuscripts using state-of-the-art technologies. Code should be original and demonstrate a development in research. It should also have clear design and be reproducible, reusable, extensible and maintainable. Manuscripts presenting code, software or implementation of described algorithms need to include a link to a repository where the code can be downloaded. In such cases the open source license should be clearly indicated in submitted manuscripts. Manuscripts that describe code that is not open source are desk rejected. The journal editors offer to fork source code or data repositories that accompany published papers on GitHub https://github.com/CAGEO, to help the community find the author's original repository.

Paper Types and maximum lengths (lengths mentioned below are not including abstract, references and figure captions):

    Research paper (5,000 words): Providing a novel and original contribution to the scientific fields of study outlined above.
    Case study (5,000 words): Describing a real-world case study on the scientific fields of study outlined above.
    Review paper (10,000 words): Critically describing the state-of-the art of applications of computer science in the geosciences, as a stand-alone contribution or to frame a special issue. Criteria for assessment shall be: completeness, depth, novelty, timeliness, quality, and interest to the Journal's readership. Before submitting review paper manuscripts, a review outline should be approved by one of the editors of the Journal.
    Book or software reviews (1500 words): Describing and evaluating a new or significant publication or piece of software, not written by the author, that is relevant to computation or informatics in the geosciences.
    Letter to the Editor: Commenting on published articles. Criteria for assessment shall be the merit of the question or comment raised. The author(s) of the commented-on article shall be offered the opportunity to prepare a reply, to be published alongside the comment.
Last updated by Dou Sun in 2020-03-13
Special Issues
Special Issue on Emerging Trends in Big Data Analytics and Natural Disasters Computers & Geosciences
Submission Date: 2020-09-01

The analysis of massive data is one of the most challenging tasks that data scientists are facing nowadays. Much effort is currently being put into the development of new approaches that can extract useful information from huge datasets in a way which is both efficient and effective. Geosciences is one of the disciplines that is benefiting the most from these advances since the processing of high-resolution satellite, aerial images, very large time series or even the information fusion of several sources are leading to the development of very powerful models. Natural disasters are extreme and unexpected threats resulting from natural processes of the Earth that can cause enormous human and economic losses. Among these destructive events, earthquakes, tsunamis, volcanic eruptions, hurricanes, tornadoes and floods stand out. Their prediction and characterization have been addressed from many different points of view but, due to the emerging big data techniques, much research is currently being conducted in this field. Hence, automated machine learning and deep learning methods for extracting relevant patterns, high performance computing or data visualization are being widely and successfully applied to many geoinformatics-related issues. Although it is almost impossible to prevent natural disasters, several actions can be taken to mitigate their effects and minimize casualties and economic losses. Thus, the discovery of precursory patterns or the development of predictive models may help to deploy emergency policies or trigger adequate alarms so that regions can be evacuated. Another relevant issue lies in the estimation of affected elements at risk, their corresponding damage potentials and the potential losses. For all the aforementioned, we kindly invite the Scientific Community to contribute to this special issue, by submitting novel and original work addressing one or more of the following topics related to natural hazards, in the context of machine learning and big data: New methods for the characterization and discovery of precursory patterns. New methods for predicting the occurrence of natural disasters. New methods for risk assessment and losses estimation. Case studies describing relevant findings with a clear interest for the Scientific Community. Finally, the authors are strongly encouraged to share codes and data to allow the experiments to be reproduced. Guest editors Francisco Martínez-Álvarez*, Data Science & Big Data Lab, Pablo de Olavide University, Spain Rudolf Scitovski, Department of Mathematics, University of Osijek, Croatia Cristina Rubio-Escudero, Department of Computer Science, University of Seville, Spain Antonio Morales-Esteban, Department of Building Structures and Geotechnical Engineering, University of Seville, Spain Relevant dates Submission deadline: September 1st, 2020. Please note that papers will be handled as they are submitted, so notifications will be sent just after the reports are received.
Last updated by Dou Sun in 2020-04-15
Special Issue on Data and Information Services for Interdisciplinary Research and Applications in Earth Science
Submission Date: 2020-09-15

Making heterogeneous Earth science data easily accessible in an integrated environment, such as online visualization and analysis services without needing to download data and software, is essential to broaden user communities. This effort is especially important to the interdisciplinary research and application communities, providing data and information to end-users with different backgrounds. To date, accessing interdisciplinary datasets is still a challenge to researchers and application users, as often reported in the literature and scientific meetings. The complexity of interdisciplinary research and applications is driven by scientific, technical, and cultural challenges. From problem definition to disseminating results to diverse audiences, the role of cross-discipline collaborations is increasingly important in a changing world. To this end, adopting FAIR principles (Findable, Accessible, Interoperable, Reusable) is a key step towards sustainable and relevant solutions. This concept encapsulates much of what this thematic number seeks to address. The development of stable data services and repositories, with persistent access methods is pivotal to ensure findability and the development of solutions downstream. Research projects, both interdisciplinary and otherwise, play an important role in this process when supported by sustainable and open data management plans. In a rapidly expanding science data service ecosystem, ‘black-box’ processors and poorly documented datasets hinder the penetration of the products across domains and publics. As such, the development of robust, well documented, and validated methodologies is paramount to ensure interoperability and reliable reusability for the full exploitation of the potential offered by cross-domain datasets. Inter-disciplinary research and applications are critical in today’s complex world, where societal challenges break through the traditional boundaries of disciplines and scales. However, it still faces specific challenges, created by the unique association of diverse science and user communities, which are not always easy to bring together, define and characterize. As such, the development of flexible visualization and dissemination platforms capable of meeting the needs and requirements of these users is needed. At present, different standards exist in Earth science communities, creating important obstacles to inter-disciplinary research and applications, such as different data formats, structures, technical jargons, etc. Earth science communities need to work together and develop common standards for data products and software to overcome these difficulties. This thematic volume seeks innovative works, with an important focus on methodological issues, describing Earth science data and information service activities for interdisciplinary research and applications. These include (1) existing tools or data services, ongoing work/project/tool development across the complete data processing, dissemination, and uptake cycle; (2) the challenges and barriers encountered, lessons learned, experiences, and suggestions with existing tools or services, and ideas/concepts for future data and information services; (3) emerging solutions for Earth science data analysis and distribution including machine- and deep-learning, cloud data services and applications; or (4) natural language processing (NLP) for findable and accessible datasets for training purposes, service development, and implementation. Addressing the specific needs and challenges facing the big-data and machine-learning community and how these connect to science, education, and societal challenges. Specific topics of interest include, but are not limited to: Large Scale Climate Data Analytics; Open Government and Open Science: more than just repositories; Ecological monitoring from the local to the global scale; Ecosystem and geo services; Alert services using remote sensing, weather forecasts, and observations (including citizen-science); Education. How successful storytelling needs inter-disciplinary data and information; Disaster response, mitigation, and recovery; Data pre-processing (remote sensing) in the cloud; Science data services and platform development; Time schedule: a) First submission: January 2020 b) Submission deadline: September 15, 2020 c) Completion of review and revision process: February 2021 Guest Editors: Vasco M. Mantas, Univeristy of Coimbra, vasco.mantas @dct.uc.pt Zhong Liu, George Mason University Fairfax / NASA GSFC GES DISC (CSISS, George Mason University), zhong.liu-1@nasa.gov Jennifer C. Wei, NASA Goddard Space Flight Center, jennifer.c.wei@nasa.gov Menglin Jin, University of Maryland - College Park, mjin1@umd.edu
Last updated by Dou Sun in 2020-04-15
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