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 http://www.geodoi.ac.cn/: 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 (https://www.mdpi.com/journal/remotesensing/special_issues/jointSI_deeplearning). 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.)
Keywords
Remote sensing
Deep learning
Sample datasets
Big data analysis
Image processing algorithms
Remote sensing applications