Special Issue "Technological Developments of Unmanned Aerial Systems (UAS) for Remote Sensing Applications"

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

Deadline for manuscript submissions: closed (31 January 2021).

Special Issue Editors

Dr. David R. Green
E-Mail Website
Guest Editor
UCEMM, Department of Geography, School of Geosciences, University of Aberdeen, Aberdeen AB24 3UF, Scotland, UK
Interests: UAV; GIS; remote sensing; photogrammetry; cartography; digital mapping; coastal management; marine spatial planning; coastal ecology
Special Issues, Collections and Topics in MDPI journals
Dr. Cristina Gómez
E-Mail Website
Guest Editor
1. Fundación Cesefor, Pol. Ind. Las Casas, 42005 Soria, Spain
2. Department of Geography and Environment, University of Aberdeen, Elphinstone Road, Aberdeen, UK
Interests: environmental remote sensing; forestry; optical; radar; UAV; time series; processes of change
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Unmanned Aerial Systems (UAS), including drones and all the related technologies—payload, power systems, automation, and data analysis—have all evolved very rapidly. Whilst drones have been around for several years, their real potential is only just beginning to be realised. Initially, drones found applications utilising cameras and other sensors for monitoring, mapping and modelling, and latterly for aerial surveying. But now drones are being used for a much wider range of applications, like search and rescue, parcel delivery, and intelligent re-forestation amongst others. The growing diversity of applications is highly dependent on the development of several technologies that will allow drones to realise their full potential. This Special Issue will focus on technological developments that are now driving future UAS and remote sensing applications.

UAS technology has the ability to collect many data sets, making its quick and accurate integration challenging. Data analysis and the rapid combination of multiple datasets will be a game-changer offering many industries the ability to efficiently understand information specific to their needs. For drones that carry sensors, mining and utilising the data will require enhanced computer algorithms and programs to unpack and understand the visual information, as well as to facilitate information management.

The automation of flights, image acquisition, and information extraction - including documentation, tracking, and GIS data integration - will be in greater demand. Software developments will drive drone technology and its possibilities, and Artificial Intelligence (AI) will increasingly be incorporated at all stages of data use. At present cloud-based machine learning (deep learning and predictive analytics) is employed to identify data characteristics, with spatial datasets trained by specialized teams. Although there are already some drone-based AI solutions for image recognition/machine vision in the industry (e.g. crop scouting or building roof surveys), it is still early in the technology development cycle. Integration of drone data and workflows into predictive maintenance and service solutions, as well as enterprise asset management systems will also be developed.

Other key areas requiring solutions are battery technology, navigation and positioning, safety, security, privacy and public nuisance issues, communication, regulation, and EVLOS and BVLOS – Extended and Beyond Visual Line of Sight. Growing requirements for increased flight endurance will require better battery power and technology, or alternative sources of energy such as solar power. In addition, cameras, sensor resolution, precision, and ease of use, will all continue to be areas needing greater development.

The SI welcomes scientific papers that touch upon some or all of these technological developments in the context of environmental remote sensing applications.

Dr. David R. Green
Dr. Cristina Gómez
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 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 2400 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 automation
  • UAV technology
  • information extraction
  • artificial intelligence
  • BVLOS/EVLOS
  • power technology
  • ease of use

Published Papers (3 papers)

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Research

Article
Emergency Landing Spot Detection Algorithm for Unmanned Aerial Vehicles
Remote Sens. 2021, 13(10), 1930; https://doi.org/10.3390/rs13101930 - 15 May 2021
Viewed by 575
Abstract
The use and research of Unmanned Aerial Vehicle (UAV) have been increasing over the years due to the applicability in several operations such as search and rescue, delivery, surveillance, and others. Considering the increased presence of these vehicles in the airspace, it becomes [...] Read more.
The use and research of Unmanned Aerial Vehicle (UAV) have been increasing over the years due to the applicability in several operations such as search and rescue, delivery, surveillance, and others. Considering the increased presence of these vehicles in the airspace, it becomes necessary to reflect on the safety issues or failures that the UAVs may have and the appropriate action. Moreover, in many missions, the vehicle will not return to its original location. If it fails to arrive at the landing spot, it needs to have the onboard capability to estimate the best area to safely land. This paper addresses the scenario of detecting a safe landing spot during operation. The algorithm classifies the incoming Light Detection and Ranging (LiDAR) data and store the location of suitable areas. The developed method analyses geometric features on point cloud data and detects potential right spots. The algorithm uses the Principal Component Analysis (PCA) to find planes in point cloud clusters. The areas that have a slope less than a threshold are considered potential landing spots. These spots are evaluated regarding ground and vehicle conditions such as the distance to the UAV, the presence of obstacles, the area’s roughness, and the spot’s slope. Finally, the output of the algorithm is the optimum spot to land and can vary during operation. The proposed approach evaluates the algorithm in simulated scenarios and an experimental dataset presenting suitability to be applied in real-time operations. Full article
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Article
Sampling-Based Path Planning for High-Quality Aerial 3D Reconstruction of Urban Scenes
Remote Sens. 2021, 13(5), 989; https://doi.org/10.3390/rs13050989 - 05 Mar 2021
Cited by 2 | Viewed by 752
Abstract
Unmanned aerial vehicles (UAVs) can capture high-quality aerial photos and have been widely used for large-scale urban 3D reconstruction. However, even with the help of commercial flight control software, it is still a challenging task for non-professional users to capture full-coverage aerial photos [...] Read more.
Unmanned aerial vehicles (UAVs) can capture high-quality aerial photos and have been widely used for large-scale urban 3D reconstruction. However, even with the help of commercial flight control software, it is still a challenging task for non-professional users to capture full-coverage aerial photos in complex urban environments, which normally leads to incomplete 3D reconstruction. In this paper, we propose a novel path planning method for the high-quality aerial 3D reconstruction of urban scenes. The proposed approach first captures aerial photos, following an initial path to generate a coarse 3D model as prior knowledge. Then, 3D viewpoints with constrained location and orientation are generated and evaluated, according to the completeness and accuracy of the corresponding visible regions of the prior model. Finally, an optimized path is produced by smoothly connecting the optimal viewpoints. We perform an extensive evaluation of our method on real and simulated data sets, in comparison with a state-of-the-art method. The experimental results indicate that the optimized trajectory generated by our method can lead to a significant boost in the performance of aerial 3D urban reconstruction. Full article
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Article
Feasibility Study Using UAV Aerial Photogrammetry for a Boundary Verification Survey of a Digitalized Cadastral Area in an Urban City of Taiwan
Remote Sens. 2020, 12(10), 1682; https://doi.org/10.3390/rs12101682 - 25 May 2020
Cited by 4 | Viewed by 1959
Abstract
In conducting land boundary verification surveys in digitalized cadastral areas in Taiwan, possible parcel points must be surveyed. These points are employed in the overlap analysis and map registration of possible parcel points and digitalized cadastral maps to identify the coordinates of parcel [...] Read more.
In conducting land boundary verification surveys in digitalized cadastral areas in Taiwan, possible parcel points must be surveyed. These points are employed in the overlap analysis and map registration of possible parcel points and digitalized cadastral maps to identify the coordinates of parcel points. Based on the computed horizontal distance and angle between control points and parcel points, parcels are staked out using ground surveys. Most studies survey possible parcel points using ground surveys with, for example, total stations. Compared with ground surveys, UAV (Unmanned Aerial Vehicle) aerial photogrammetry can provide more possible parcel points. Thus, an overlap analysis of digitalized cadastral maps, combined with the collection of possible parcel points, will be more comprehensive. In this study, a high-quality-medium format camera, with a 55 mm focal length, was carried on a rotary UAV to take images, with a 3 cm ground sampling distance (GSD), flying 300 m above the ground. The images were taken with an 80% end-lap and side-lap to increase the visibility of the terrain details for stereo-mapping. According to the test conducted in this study, UAV aerial photogrammetry can accurately provide supplementary control points and assist in the boundary verification of digitalized cadastral areas in Taiwan. Full article
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