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European Journal of Remote Sensing

Special Issue

Included in the Directory of Open Access Journals (DOAJ), the journal publishes research on all applications of active or passive remote sensing technologies related to terrestrial, oceanic, and atmospheric environments.

Deep Learning for Remote Sensing Environments

European Journal of Remote Sensing, Special Issue

Visit Journal Articles

Remote sensing can be considered as one of the vastly researched fields currently. Remote sensing technologies basically work with certain principals, such as acquiring the data from an object which is not connected directly to the object. The data collected and analyzed by the remote sensors are being utilized in various geographically linked sectors. In remote sensing environment, gathered information from the sensors are used for management of natural resources, development of sustainability, identification and management of disasters, prevention of environmental degradation and others.

Increase in the population has increased the monitoring challenges of the environment. The need of remote sensing for the purpose of the environment has been growing rapidly and its scale is being increased. Some of the advanced sensing techniques like satellite, aircraft, drone, ships etc. are also being used in the study of measurement of land covers, understanding the habitats, melting of glaciers, water resources and many more. When certain learning techniques are implemented furthermore to the data collected from the sensing devices the study becomes broader and diversified.

Deep learning is such a learning process where the raw data can be converted into various levels of meaningful data. The data received from the sensing devices can be proper labelled or unlabelled, by using certain deep learning methodologies they can be classified accordingly. When deep learning is implemented in the remote sensing environments the real need and the purpose of data collection can be experienced. There are many interests created in the development of remote sensing areas with the methodological approaches of deep learning. Deep Learning has all the possibilities of competing and completing the various challenges faced in the remote sensing environment.

This special issue will analyze the latest advances and developments concerning the prospectus of deep learning in remote sensing environment. It can and will create an analytical platform the researchers to discuss both theoretical and applicative nature of the remote sensing-based environment, it will also tend to contribute new deep learning approaches involved in the development of the remote sensing community.


Topics of interest include but are not restricted to:

  • Deep Reinforcement Learning for Remote Sensing Images
  • Deep Learning on Land cover classification
  • Deep learning on remote sensors for activity recognition
  • Deep Learning for crop yield prediction based on remote sensing data
  • Deep learning on advanced data analytics for large-scale remote sensing
  • Deep learning in Remote Sensing and Geo Informatics Applications
  • A comparative study of conventional and deep learning approaches for Remote Sensing environment
  • Deep learning on small, medium and high-resolution remote sensing images from different sensors
  • Remote sensing for disaster assessment using deep learning methods
  • Supervised and unsupervised representation learning for remote sensing environment
  • Classification of remote sensing images in deep learning framework
  • Deep learning approaches for advanced critical prediction system using remote sensing data

Guest Editors

 

Gunasekaren Manogaran
University of California, Davis
gmanogaran@ucdavis.edu

Hassan Oudrat-Ullah
York University
hassanq@yorku.ca

Bharat S. Rawal Kshatriya
Pennsylvania State University
bsr17@psu.edu

Important Dates

 

Full paper submission deadline
October 5, 2019

Initial decisions on revisions or rejection
December 25, 2019

Final manuscripts submitted to EuJRS
February 27, 2020

Expected publication date
April 30, 2020

Articles will be available online approximately one month after acceptance, final technical review and the completion of revisions for compliance with journal format.

If you have any specific questions about the special issue, please contact the guest editors listed.

How to Submit your Manuscript

 

All submissions will be peer reviewed according to the European Journal of Remote Sensing (EuJRS) guidelines. Submitted manuscripts should not have been published 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://mc.manuscriptcentral.com/tejr. Please choose 'EuJRS Special Issue Paper' from the 'Manuscript Type' picklist on the submission form, irrespective of the paper type (i.e. even if your paper would normally be classified as a Research Paper, or Review Article). Also, please enter “Deep Learning for Remote Sensing Environments” in the space provided on the submission form for the name of the special issue.

EuJRS is an Open Access journal. The standard article publishing charge (APC) will be: $700 / £540 / €620, The APC for members of AIT/EARSeL is $490 / £380 / €435. Depending on your location, these charges may be subject to local taxes.

Should you have any submission questions, please contact Patrizia Rossi, Administrator of EuJRS at: patrizia.rossi@unifi.it