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

Dear Colleagues,

In the last few years, remote sensing has entered the era of big data characterized by “volume, velocity, variety, and value”. Deep learning has 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 for original outcomes from research activities and aims to publish simultaneously remote sensing sample datasets and the description of related algorithms or applications from the same research team or scholars. Our aim is for the jointly published papers to 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.

The outcomes of a research activity are not only a discovery paper, but a relevant research data and data paper. Supported by National Earth Observation Data Center (NODA), 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 and openly accessible.

The deadline for dataset and data paper submissions to Global Change Research Data Publishing & Repository and the Journal of Global Change Data & Discovery (http://www.geodoi.ac.cn/WebEn/IssuesInfo.aspx?ID=202001) is 31 June 2020.

Potential topics include but are not limited to:

• Remote sensing data sample datasets and descriptions for deep learning (e.g., datasets on land cover, disasters, agriculture, buildings, transportation infrastructure, ships);
• Innovative deep learning algorithms for remote sensing data processing (e.g., object or target detection, classification, parameter adaptation);
• Training and testing deep learning algorithms and solutions to remote sensing problems;
• Deep learning for image processing and classification;
• Deep learning for image understanding including semantic labeling, object detection, or image retrieval;
• Deep learning for remote sensing data fusion;
• Deep learning with scarce or low-quality remote sensing data across resolutions or sensors;
• Deep learning for time-series applications;
• Applications of deep learning in remote sensing (e.g., disaster assessment, agricultural monitoring, urban planning).

Prof. Guoqing Li
Prof. Bing Zhang
Prof. Dr. Thomas Blaschke
Dr. Junshi Xia
Prof. Chuang Liu
Prof. Carol Song
Prof. Philippe De Maeyer
Prof. Yifang Ban
Dr. Xiaochuang Yao
Dr. Amani J. Uisso

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.

• Remote sensing
• Deep learning
• Sample datasets
• Big data analysis
• Image processing algorithms
• Remote sensing applications