Special Issue "Advances and Innovative Applications of Unmanned Aerial Vehicles"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: 30 June 2020.

Special Issue Editor

Dr. Ming-Der Yang
E-Mail Website
Guest Editor
Distinguished Professor & Chair, Department of Civil Engineering, National Chung Hsing University, Taichung, Taiwan
Interests: image processing; AI; UAVs

Special Issue Information

Dear Colleagues,

Theme and Scope

Recently, unmanned aerial vehicles (UAVs) are rapidly growing in remote sensing with their autonomy, flexibility, and broad range of application domains. UAVs offer possible civil and public domain applications in which single or multiple UAVs may be employed, including 3D scene reconstruction, imaging and mapping, utility inspection, anomaly detection and prevention, hazard monitoring, precision agriculture, etc. The prospective UAV applications are expected to be more broadly expanded in various fields, while deep learning technology is applied. Moreover, UAV applications will become more promising once 5G and AI technologies are applied in the near future. Aiming to promote an international exchange of scientific knowledge and experience in the field of UAV applications, we propose a Special Issue “Advances and Innovative Applications of Unmanned Aerial Vehicles”. This Special Issue will bring together academic and industrial papers in the field of UAVs to share the techniques and applications of consumer drones. We expect to publish 15+ valuable papers related to precious research outcomes. This Special Issue will provide a valuable opportunity to exchange ideas and expertise as well as network with research groups worldwide. Prospected authors are invited to submit their original contributions, surveys, and case studies that address novel research on and valuable applications of UAVs.

 The main topics include but are not limited to: 

  • 3D scene reconstruction of UAVs;
  • 5G era for UAV communications;
  • Ad-hoc networking and sensor networks for UAVs;
  • Autonomous UAVs;
  • Artificial intelligence in UAVs;
  • Big data and machine learning for UAVs;
  • Communication and control architectures for UAVs;
  • Cyber and physical security of UAVs.
  • Energy efficiency and optimization for UAV routing;
  • High-resolution UAV image processing and applications;
  • Optimal deployment for UAV swarm service;
  • Spectrum sensing techniques for UAV detection;
  • Simultaneous localization and mapping for UAVs;
  • Tracking, localization, navigation, and dynamic path planning of UAVs.

Dr. Ming-Der Yang
Guest Editor

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 2000 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.


  • UAV
  • image processing
  • AI
  • deep learning

Published Papers (1 paper)

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Open AccessArticle
Robust Motion Control for UAV in Dynamic Uncertain Environments Using Deep Reinforcement Learning
Remote Sens. 2020, 12(4), 640; https://doi.org/10.3390/rs12040640 - 14 Feb 2020
In this paper, a novel deep reinforcement learning (DRL) method, and robust deep deterministic policy gradient (Robust-DDPG), is proposed for developing a controller that allows robust flying of an unmanned aerial vehicle (UAV) in dynamic uncertain environments. This technique is applicable in many [...] Read more.
In this paper, a novel deep reinforcement learning (DRL) method, and robust deep deterministic policy gradient (Robust-DDPG), is proposed for developing a controller that allows robust flying of an unmanned aerial vehicle (UAV) in dynamic uncertain environments. This technique is applicable in many fields, such as penetration and remote surveillance. The learning-based controller is constructed with an actor-critic framework, and can perform a dual-channel continuous control (roll and speed) of the UAV. To overcome the fragility and volatility of original DDPG, three critical learning tricks are introduced in Robust-DDPG: (1) Delayed-learning trick, providing stable learnings, while facing dynamic environments; (2) adversarial attack trick, improving policy’s adaptability to uncertain environments; (3) mixed exploration trick, enabling faster convergence of the model. The training experiments show great improvement in its convergence speed, convergence effect, and stability. The exploiting experiments demonstrate high efficiency in providing the UAV a shorter and smoother path. While, the generalization experiments verify its better adaptability to complicated, dynamic and uncertain environments, comparing to Deep Q Network (DQN) and DDPG algorithms. Full article
(This article belongs to the Special Issue Advances and Innovative Applications of Unmanned Aerial Vehicles)
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