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Deep Learning Applications of 3D Reconstruction and Visualization from Remote Sensing Imagery

This special issue belongs to the section “AI Remote Sensing“.

Special Issue Information

Dear Colleagues,

Deep learning applications arise and thrive in various fields, including education, healthcare, marketing and advertising, cybersecurity, and natural language processing. However, the number of applications, new approaches, and network architectures has grown rapidly, especially in remote sensing. Related research ranges from automation, enhanced spatial understanding, disaster management, and robotics to fundamental research.

Even if some algorithms and approaches have been known for decades, exciting new approaches are constantly emerging from various combinations and are being developed.

This Special Issue aims to cover recent advancements in deep learning methods in the field of 3D reconstruction and geo-visualization. Both original research and review articles are welcome. Topics include, but are not limited to, the following:

  •     Multi-spectral and hyperspectral remote sensing;
  •     Lidar and laser scanning;
  •     Geometric reconstruction;
  •     Physical modeling and signatures;
  •     Change detection;
  •     Image processing and pattern recognition;
  •     Remote sensing applications.

Prof. Dr. Henry Meißner
Prof. Dr. Francesco Nex
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. Remote Sensing 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 2700 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
  • 3D reconstruction
  • visualization
  • disaster management
  • enhanced spatial understanding
  • algorithms

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Remote Sens. - ISSN 2072-4292