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Submit a Manuscript to the Journal
European Journal of Remote Sensing

For a Special Issue on
Deep Learning for Earth Resource and Environmental Remote Sensing

Manuscript deadline
30 September 2021

Cover image - European Journal of Remote Sensing

Special Issue Editor(s)

Professor Carlos Enrique Montenegro Marin, District University Francisco José de Caldas, Colombia
[email protected]

Dr Xuyun Zhang, Macquarie University, Australia
[email protected]

Dr Nallappan Gunasekaran, Shibaura Institute of Technology, Japan
[email protected]

Visit JournalArticles

Deep Learning for Earth Resource and Environmental Remote Sensing

Today, we live in the age of technological advancement, where remote sensing and image analysis have become crucial for monitoring the environment and other natural resources. In general, remote sensing deals with analyzing objects at a distance, without the establishment of direct contact. However, the growing population and various anthropogenic activities have placed increased pressure on the environment, creating the requirement for sustainable surveillance methodologies. Deep learning in the context of remote sensing has gained massive popularity in recent years, and it is widely used for the purpose of real-time image analysis. Basically, deep learning algorithms are developed for vision-related problems. Later, the feasibility of these models enhanced its application towards the various remote sensing use cases such as earth resource monitoring and environmental observation. However, it gives rise to numerous research questions such as, what are the advantages of deep learning approaches for earth-resource monitoring and environmental observation? Does the deep learning model is appropriate to deal with the high resolution of the satellite imagery data? How efficiently it advances the state-of-the-art? What will be the outcomes of the deep learning model in terms of performance measures? What will be the potential limitations? What are the potential opportunities of deep learning for earth resource monitoring and environmental observation?

Effectively addressing the above-mentioned research gaps will significantly make using deep learning techniques for remote sensing applications a greater success. In this regard, this special issue aims to explore fundamental and applied research on deep learning for remote sensing. Researchers and industrial professionals from computer science and information technology are most invited to submit their novel and innovative research works in this thematic background.

Topics of interest include, but are not limited to:

  • Deep learning satellite and earth image processing
  • Advances in deep belief networks for earth observation and natural resource monitoring
  • Deep learning assisted image fusion for multispectral image processing
  • Advances in deep transfer learning for remote monitoring
  • Frontiers in deep learning and computer vision for earth observation and satellite imaging
  • Deep learning for automated earth data analysis and prediction
  • Deep learning for spatial-temporal image processing
  • Deep learning for 3D object classification and detection
  • Deep learning multi-modal and multi-scale data analysis
  • Trends in deep learning for solid earth observation, modelling and understanding.

Submission Instructions

All submissions undergo single-blind peer review according to the European Journal of Remote Sensing (EuJRS) guidelines. Submitted manuscripts should not have been published previously or be under review elsewhere.

Prospective authors should consult the Instructions for Authors page on the journal website for guidelines and information on paper formatting and submission.

Authors should submit manuscripts using the EuJRS manuscript central system at https://rp.tandfonline.com/submission/create?journalCode=TEJR.

Select Yes to the question “Are you submitting your paper for a specific special issue?” and enter the exact title of the special issue.

EuJRS is an Open Access journal. The standard article publishing charge (APC) will be: $1,000 / £770 / €880. Depending on your location, these charges may be subject to local taxes.

AIT members are eligible for a 30% discount, with a charge of $700 / £540 / €615 applying. AIT members wishing to take advantage of this discounted rate should contact Patrizia Rossi at [email protected] to obtain a discount code prior to submission.

EARSeL members are eligible for a 15% discount on regular submissions, with a charge of $850 / £655 / €750 applying. EARSeL members wishing to take advantage of this discounted rate should contact Heide Bierbrauer at [email protected] to obtain a discount code prior to submission.

Should you have any submission questions, please contact Patrizia Rossi, Administrator of EuJRS at: [email protected]