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Call for Papers for a Joint Special Issue on "Deep Learning in Remote Sensing: Sample Datasets, Algorithms and Applications"

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Call for Papers for a Joint Special Issue on

Deep Learning in Remote Sensing: Sample Datasets, Algorithms and Applications PDF


The outcomes of a research activity are not only a discovery paper, but relevant research data and data paper. We invite original results of a research activity for a joint special issue of three publishers, including Global Change Research Data Publishing & Repository (DOI:10.3974, Regular member of the World Data System) for publishing datasets and two journals, Global Change Research Data & Discovery (ISSN 2096-3645) for publishing data papers and Remote Sensing (ISSN 2072-4292) for publishing discovery papers based on the relevant datasets and data papers. All of the datasets, data papers and discovery papers are peer-reviewed openly accessible.


Submissions should consist of a set of one dataset and two relevant papers: first, a data paper and its dataset should be submitted to Global Change Research Data Publishing & Repository. Upon approval, the related discovery paper based on such dataset should be submitted to the journal of Remote Sensing.


Deadline for dataset and data paper submissions to Global Change Research Data Publishing & Repository and Journal of Global Change Data & Discovery at 31 March 2020.


The data papers accepted by Journal of Global Change Data & Discovery will be invited to submit the related research paper to Remote Sensing ( Deadline for manuscript submissions to Remote Sensing at 31 October 2020.

Special Issue Editors

Lead Guest Editor

Prof. Guoqing Li

Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China

Guest Editor

Prof. Bing Zhang

Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China

Prof. Dr. Thomas Blaschke

Interfaculty Department of Geoinformatics - Z GIS, University of Salzburg, Salzburg, A-5020, Austria

Dr. Junshi Xia

Geoinformatics Unit, RIKEN Center for Advanced Intelligence Project, Nihonbashi 1-chome Mitsui Building, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan

Prof. Chuang Liu

Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

Prof. Carol Song

RCAC Rosen Center for Advanced Computing, Purdue University, West Lafayette, IN 47907, USA

Prof. Phillipe De.Meayer

Department of Geography, Ghent University, 9000 Gent, Belgium

Prof. Yifang Ban

Division of Geoinformatics and Department of Urban Planning and Environment at KTH Royal Institute of Technology in Stockholm, Sweden

Dr. Xiaochuang Yao

College of Land Science and Technology, China Agricultural University, Beijing, 100083, China

Dr. Amani J. Uisso

Tanzania Forestry Research Institute (TAFORI), Tanzania

Special Issue Information


In the last few years, remote sensing has entered the era of big data characterized by “volume, velocity, variety, and value.” Deep learning has been proven to be efficient for large remote sensing data sets, particularly for feature or target detection, and for image and data classification. Deep learning-based applications are also emerging in various domains, such as disaster assessment, agricultural monitoring, and urban planning. Still, strategies for the creation of massive sample datasets and for the construction of deep learning networks play essential roles in the success of deep learning. Researchers have developed a number of marker sample datasets for object detection and image classification, which have supported successful applications of deep learning in remote sensing. Hence, the joint publication and release of these sample databases and related algorithms or applications will undoubtedly promote the further development of deep learning in the field of remote sensing and will increase transparency, transferability and reproducibility.

This Joint Special Issue calls the original outcomes from research activities and to publish simultaneously remote sensing sample datasets and the description of related algorithms or applications from the same research team or scholars. We aim that the jointly published papers will promote a transparent use of deep learning in remote sensing, as well as sharing of high-precision sample datasets while simultaneously documented through the corresponding papers of the joint special issue.

Potential topics include, but are not limited to:

l  Remote sensing data sample datasets and descriptions for deep learning (e.g., datasets on land cover, disasters, agriculture, buildings, transportation infrastructure, ships, etc.)

2  Innovative deep learning algorithms for remote sensing data processing (e.g., object or target detection, classification, parameter adaptation, etc.)

3  Training and testing deep learning algorithms and solutions to remote sensing problems;

4  Deep learning for image processing and classification

5  Deep learning for image understanding including semantic labeling, object detection, or image retrieval

6  Deep learning for remote sensing data fusion

7  Deep learning with scarce or low-quality remote sensing data across resolutions or sensors

8  Deep learning for time-series applications

9  Applications of deep learning in remote sensing (e.g., disaster assessment, agricultural monitoring, urban planning etc.)


Remote sensing

Deep learning

Sample datasets

Big data analysis

Image processing algorithms

Remote sensing applications