Unmanned Aerial Systems (UAS) for Global Challenges: Current Technologies and Future Prospects

A special issue of Drones (ISSN 2504-446X).

Deadline for manuscript submissions: 31 July 2024 | Viewed by 2436

Special Issue Editors


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Guest Editor
Earth Observation Science, ITC Faculty, University of Twente, 7514 AE Enschede, The Netherlands
Interests: photogrammetry; geomatics; mobile mapping systems
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands
Interests: geometric and radiometric sensors; sensor fusion; calibration of imageries; signal/image processing; mission planning; navigation and position/orientation; machine learning; simultaneous localization and mapping; regulations and economic impact; agriculture; geosciences; urban area; architecture; monitoring/change detection; education; unmanned aerial vehicles (UAV)
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
3D Optical Metrology (3DOM) Unit, Bruno Kessler Foundation (FBK), 38123 Trento, Italy
Interests: photogrammetry; laser scanning; optical metrology; 3D; AI; quality control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Unmanned aerial systems (UAS) are currently a hot topic of research and education, with research being stimulated by industry and commerce all over the world. Interestingly, UAS have diverse uses for public safety and for managing the current global challenges of urbanization, climate change, natural disasters, and many more. An added challenge is that no two incidents are ever the same, whether one is tracking a wildfire, conducting a search and rescue expedition, or surveying building damage.

Moreover, UAS can be used for flood mapping and risk estimation, wildfire or volcanic lava flow monitoring, damaged buildings assessment, debris volume calculation, slums and informal settlements or land use change mapping, etc.

Given these possibilities, research innovations, new tools, and best practices in UAS data collection, processing, and modeling are being shared and studied in a constantly evolving field to identify the most efficient and successful solutions to the global challenges we face. We believe that this communication will be useful for a variety of challenging applications, allowing for fresh research and analysis.

The Special Issue aims to collect and present modern and innovative research in UAS technologies, concepts, and methodologies for the acquisition and processing of collected data related to ongoing global challenges and societal problems.

It is our aim to encourage collaboration and the sharing of best practices related to UAS technologies across a range of disciplines. Researchers, developers, and scientists from different scientific disciplines of geomatics, geoinformatics, geology, remote sensing, robotics, mapping, cultural heritage, agriculture, and other related fields are, therefore, invited to present their latest scientific work.

We encourage original research contributions related, but not necessarily restricted to:

  • Innovative techniques in using unmanned aerial systems (UAS) for data acquisition and processing.
  • Autonomous UAS flight missions.
  • UAS data acquisition and navigation in GNSS-denied conditions.
  • Direct georeferencing potentials.
  • Deep learning methods used to process UAS datasets (feature extraction, point cloud classification, etc).
  • Cloud-based and big data solutions for UAS.
  • On-board real-time UAS data processing and manipulation.
  • Challenges and best practices in UAS-based multispectral and hyperspectral imaging.
  • UAS hybrid sensor systems and data fusion.
  • UAS-based solutions for digital twins and virtual and augmented reality.
  • Standardization and quality control for UAS-based 3D mapping.
  • UAS-based applications for monitoring, documenting, and mapping forestry, infrastructures, wildfire, flooding, landslide, damages, natural hazards, etc.
  • Review articles extensively covering one or more of the above-mentioned topics.

Dr. Bashar Alsadik
Dr. Francesco Nex
Dr. Fabio Remondino
Dr. Jesús Balado Frías
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. Drones is an international peer-reviewed open access monthly 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 2600 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.

Keywords

  • UAS, UAV, RPAS, and drones
  • natural disasters
  • big data
  • geodata collection and processing
  • machine and deep learning
  • three-dimensional mapping

Published Papers (1 paper)

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Research

15 pages, 1855 KiB  
Article
End-to-End Nano-Drone Obstacle Avoidance for Indoor Exploration
by Ning Zhang, Francesco Nex, George Vosselman and Norman Kerle
Drones 2024, 8(2), 33; https://doi.org/10.3390/drones8020033 - 24 Jan 2024
Viewed by 2029
Abstract
Autonomous navigation of drones using computer vision has achieved promising performance. Nano-sized drones based on edge computing platforms are lightweight, flexible, and cheap; thus, they are suitable for exploring narrow spaces. However, due to their extremely limited computing power and storage, vision algorithms [...] Read more.
Autonomous navigation of drones using computer vision has achieved promising performance. Nano-sized drones based on edge computing platforms are lightweight, flexible, and cheap; thus, they are suitable for exploring narrow spaces. However, due to their extremely limited computing power and storage, vision algorithms designed for high-performance GPU platforms cannot be used for nano-drones. To address this issue, this paper presents a lightweight CNN depth estimation network deployed on nano-drones for obstacle avoidance. Inspired by knowledge distillation (KD), a Channel-Aware Distillation Transformer (CADiT) is proposed to facilitate the small network to learn knowledge from a larger network. The proposed method is validated on the KITTI dataset and tested on a Crazyflie nano-drone with an ultra-low power microprocessor GAP8. This paper also implements a communication pipe so that the collected images can be streamed to a laptop through the on-board Wi-Fi module in real-time, enabling an offline reconstruction of the environment. Full article
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