sensors-logo

Journal Browser

Journal Browser

Deep Learning for Resource and Environmental Earth Observations

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (25 January 2025) | Viewed by 365

Special Issue Editors

School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
Interests: implicit modeling; 3D prospectivity modeling; machine learning; 3D GIS
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
School of Land Science and Technology, China University of Geosciences, Beijing 100083, China
Interests: remote imagery processing; environmental monitoring; deep learning

E-Mail Website
Guest Editor
School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
Interests: 3D prospectivity modelling; spatial analysis; numerical simulation; economic geology; mineral system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The transformation in resource exploration and environmental monitoring has been driven by advancements in sensing technologies, ranging from remote sensing to deep drilling data, resulting in a significant shift from being a data-poor to a data-rich field. The increasing availability of extensive Earth observation data presents immense potential for deep learning. Deep learning has reached a transformative level of maturity in Earth resource and environmental remote sensing. Its ability to automatically learn complex patterns from vast amounts of Earth observation data, combined with advances in computational power, has led to breakthroughs with numerous applications currently in active development worldwide. As this rapid advancement continues, novel deep learning techniques, from convolutional neural networks to transformer networks, are emerging, marking a new era in data-driven resource exploration and environmental Earth observation.

This Special Issue focuses on the latest advancements in Earth resource and environmental remote sensing. We invite contributions across all areas of deep learning for resource and environmental Earth observations, including deep learning models, algorithms, and innovative applications that leverage Earth observation data for resource exploration and environmental monitoring. Topics of interest include, but are not limited to, the following:

  • Deep learning models for inverting Earth observation data related to resource and environmental systems.
  • Deep learning models for fusing multimodal resource and environmental data.
  • Deep learning models for exploring geological and resource systems.
  • Deep learning-driven resource potential modeling and exploration.
  • Deep learning-empowered environmental modeling, data assimilation, and parameter estimation.
  • Deep learning methods for environmental monitoring.
  • Deep mining of Earth observation data on resource and environmental systems.
  • New insights gained through deep learning into resource and environmental systems.
  • New services and applications that integrate deep neural networks with resource and environmental data.

Dr. Hao Deng
Dr. Yuebin Wang
Dr. Zhankun Liu
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. Sensors 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 2600 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

  • deep learning models
  • resource and environmental system
  • resource exploration
  • resource potential modeling
  • environmental monitoring
  • deep mining of earth observation data

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers

There is no accepted submissions to this special issue at this moment.
Back to TopTop