The overall aim of this special issue is to collect state-of-the-art research efforts on the latest development, up-to-date issues, and challenges in the analysis and processing of UAV data using different kinds of computing architectures, with particular emphasis on advanced machine (and deep) learning algorithms and scalable implementations. Submissions to the special issue should describe original, innovative and in-depth research that makes significant theoretical, methodological or practical contributions to this specific field. Potential topics of interest include, but are not limited to the following areas:
- Advanced algorithms for UAV data interpretation.
- Machine learning based UAV data processing.
- Deep learning based UAV data processing and its lightweight design.
- Parallel processing for efficient analysis of UAV data.
- Specialized architectures for real-time processing of UAV data.
- Cloud computing technologies for UAV data processing.
- Distributed storage solutions for UAV data.
- Stochastic optimization for UAV data processing.
- New techniques and applications for fusion of UAV data with other data sources, including optical (e.g., hyperspectral), radar (e.g., synthetic aperture radar) and ligh detection and ranging (LiDAR).
- Classification strategies for UAV data.
- Change detection for UAV data.
|Jun Li Sun |
University of Extremadura
Southwest Jiaotong University,
|Manuscript Submission||Manuscript Review||Estimated Publication|
|April 1 – June 30, 2020||As received – October 30, 2020||1Q 2021|
Manuscripts with supporting documents are submitted to the web-based portal: https://mc.manuscriptcentral.com/jmass. After registering with the site, Author Instructions provide detailed guidelines for manuscript preparation. All authors of a paper must each link a valid ORCID ID to their personal page on the system. Submission requirements include:
- A Cover Letter clearly identifying the paper as a submission for the Special Issue.
- The manuscript.
- The name of the Special Issue should be included in the upper right-hand corner of every document submitted.
- Additional details available on the manuscript submission portal, https://mc.manuscriptcentral.com/jmass and on J-MASS information site, https://ieee.j-mass.org.