Special Issue "Advanced Deep Learning Techniques for Earth Observation and Applications"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: 31 December 2021.

Special Issue Editors

Prof. Dr. Zhenwei Shi
E-Mail Website
Guest Editor
Image Processing Center, School of Astronautics, Beihang University, Beijing 100191, China
Interests: target detection; machine learning; deep learning; hyperspectral image processing
Dr. Bin Pan
E-Mail Website
Guest Editor
School of Statistics and Data Science, Nankai University, Tianjin 300071, China
Interests: hyperspectral image processing; deep learning; domain adaptation
Dr. Shuo Yang
E-Mail Website
Guest Editor
HiWing Satellite Operation Division, The Third Institute of China Aerospace Science and Industry Corporation (CASIC), Beijing, China
Interests: hyperspectral image processing and pattern recognition

Special Issue Information

Dear Colleagues,

Satellite sensors are of great value to Earth observation by virtue of the advantages of high-frequency revisit, high spatial coverage, and relatively low price. In recent years, the rapid growth of deep learning techniques has significantly promoted the potential for developing advanced algorithms for various remote sensing applications, such as urban monitoring, land observation and sea surveillance. However, the increasing amount of remote sensing data puts forward higher requirements on learning algorithms. How to effectively and efficiently extract information from the massive remote sensing data to assist specific applications is a promising direction.

This Special Issue aims to exploit the advanced deep learning technology to further push forward the potential of geoscience information extraction from remote sensing data. Potential topics include, but are in no way limited to:

  • Supervised/self-supervised/semi-supervised learning for remote sensing data analysis
  • High-resolution remote sensing image processing based on deep learning
  • Efficient neural networks for remote sensing data processing
  • Image classification, semantic segmentation, target detection and change detection in remote sensing images
  • Adversarial learning for remote sensing image processing

Prof. Dr. Zhenwei Shi
Dr. Bin Pan
Dr. Shuo Yang
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 papers will be 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 2400 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.


  • Earth observation
  • Deep learning
  • Image classification
  • Change detection
  • Target detection
  • Supervised learning

Published Papers (1 paper)

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Novel Intelligent Spatiotemporal Grid Earthquake Early-Warning Model
Remote Sens. 2021, 13(17), 3426; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13173426 - 29 Aug 2021
Viewed by 314
The integration analysis of multi-type geospatial information poses challenges to existing spatiotemporal data organization models and analysis models based on deep learning. For earthquake early warning, this study proposes a novel intelligent spatiotemporal grid model based on GeoSOT (SGMG-EEW) for feature fusion of [...] Read more.
The integration analysis of multi-type geospatial information poses challenges to existing spatiotemporal data organization models and analysis models based on deep learning. For earthquake early warning, this study proposes a novel intelligent spatiotemporal grid model based on GeoSOT (SGMG-EEW) for feature fusion of multi-type geospatial data. This model includes a seismic grid sample model (SGSM) and a spatiotemporal grid model based on a three-dimensional group convolution neural network (3DGCNN-SGM). The SGSM solves the problem concerning that the layers of different data types cannot form an ensemble with a consistent data structure and transforms the grid representation of data into grid samples for deep learning. The 3DGCNN-SGM is the first application of group convolution in the deep learning of multi-source geographic information data. It avoids direct superposition calculation of data between different layers, which may negatively affect the deep learning analysis model results. In this study, taking the atmospheric temperature anomaly and historical earthquake precursory data from Japan as an example, an earthquake early warning verification experiment was conducted based on the proposed SGMG-EEW. Five groups of control experiments were designed, namely with the use of atmospheric temperature anomaly data only, use of historical earthquake data only, a non-group convolution control group, a support vector machine control group, and a seismic statistical analysis control group. The results showed that the proposed SGSM is not only compatible with the expression of a single type of spatiotemporal data but can also support multiple types of spatiotemporal data, forming a deep-learning-oriented data structure. Compared with the traditional deep learning model, the proposed 3DGCNN-SGM is more suitable for the integration analysis of multiple types of spatiotemporal data. Full article
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