Special Issue "Latest Developments, Methodologies and Applications Based on UAV Platforms"

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

Deadline for manuscript submissions: closed (31 July 2018)

Special Issue Editors

Guest Editor
Dr. Francesco Nex

Department of Earth Observation,Science (EOS), ITC Faculty, University of Twente PO Box 217, 7500 AE, Enschede, The Netherlands
Website | E-Mail
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
Guest Editor
Prof. Dr. Fabio Remondino

Bruno Kessler Foundation (FBK), 3D Optical Metrology (3DOM) unit, Trento, Italy; Vice-President of EuroSDR; President of ISPRS Technical Commission II “Photogrammetry”; Vice-President CIPA Heritage Documentation
Website | E-Mail
Fax: +39 0461 314340
Interests: photogrammetry; laser scanning; 3D reconstruction; 3D modeling; sensor integration

Special Issue Information

Dear Colleagues,

Using small Unmanned Aerial Vehicles (UAV) as data acquisition platforms and autonomous or semi-autonomous measurement instruments has become attractive for many emerging applications. They represent a valid alternative or a complementary solution to traditional platforms especially for extremely high resolution acquisitions on small or inaccessible areas. Thanks to their timely, cheap and extremely rich data acquisition capacity with respect to other acquisition systems, UAVs are emerging as innovative and cost-effective devices to perform numerous urban and environmental tasks.

This Special Issue aims at collecting new developments and methodologies, best practices and applications of UAVs in Geomatics. We welcome submissions which provide the community with the most recent advancements on all aspects of UAV in Geomatics, including but not limited to:

  • Data processing and Photogrammetry
  • Navigation and position/orientation determination
  • Data analysis (image classification, feature extraction, target detection, change detection, biophysical parameter estimation, etc.)
  • Platforms and new sensors on board (multispectral, hyperspectral, thermal, lidar, SAR, gas or radioactivity sensors, etc.)
  • Data fusion: integration of UAV imagery with satellite, aerial or terrestrial data, integration of heterogeneous data captured by UAVs
  • On-line and real time processing / collaborative and fleet of UAVs applied to Geomatics
  • On-board data storage and transmission
  • UAV control, obstacle sense and avoidance
  • Autonomous flight and exploration
  • Applications (3D mapping, urban monitoring, precision farming, forestry, disaster prevention, assessment and monitoring, search and rescue, security, archaeology, industrial plant inspection, etc.)
  • Any use of UAVs related to Geomatics

This Special Issue will also feature selected papers from the UAV-g 2017 conference. Authors wishing to have their work considered for this issue, including those not able to present at the conference, should contact the Guest Editors.

Dr. Francesco Nex
Prof. Dr. Fabio Remondino
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. Drones is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) is waived for well-prepared manuscripts submitted to this issue. 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.

Published Papers (4 papers)

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Research

Open AccessArticle Unmanned Aerial Vehicles (UAV) Photogrammetry in the Conservation of Historic Places: Carleton Immersive Media Studio Case Studies
Received: 2 April 2018 / Revised: 8 May 2018 / Accepted: 16 May 2018 / Published: 18 May 2018
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Abstract
The increasing commercialization of unmanned aerial vehicles (UAVs) has opened the possibility of performing low-cost aerial image acquisition for the documentation of cultural heritage sites through UAV photogrammetry. This paper presents two case studies that illustrate the use of the DJI Phantom 4
[...] Read more.
The increasing commercialization of unmanned aerial vehicles (UAVs) has opened the possibility of performing low-cost aerial image acquisition for the documentation of cultural heritage sites through UAV photogrammetry. This paper presents two case studies that illustrate the use of the DJI Phantom 4 normal UAV for aerial image acquisition, and the results that can be achieved using those images. A general workflow procedure of oblique image capturing and data processing of large data sets has been illustrated in the Prince of Wales Fort case study to create photogrammetric models and to generate orthophotos for condition assessment applications. The second case study provides insight on the possibility of using UAVs for post-disaster documentation when the accessibility and the availability of high cost equipment is of major concern. The results that were obtained from UAV photogrammetry of Nyatapola Temple and Bhairabnath Temple in Taumadhi Square in Nepal, which were damaged by the 2015 Gorkha earthquake, are presented and discussed. Full article
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Open AccessArticle Use of UAV-Borne Spectrometer for Land Cover Classification
Received: 20 March 2018 / Revised: 13 April 2018 / Accepted: 17 April 2018 / Published: 20 April 2018
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Abstract
Unmanned aerial vehicles (UAV) are being used for low altitude remote sensing for thematic land classification using visible light and multi-spectral sensors. The objective of this work was to investigate the use of UAV equipped with a compact spectrometer for land cover classification.
[...] Read more.
Unmanned aerial vehicles (UAV) are being used for low altitude remote sensing for thematic land classification using visible light and multi-spectral sensors. The objective of this work was to investigate the use of UAV equipped with a compact spectrometer for land cover classification. The UAV platform used was a DJI Flamewheel F550 hexacopter equipped with GPS and Inertial Measurement Unit (IMU) navigation sensors, and a Raspberry Pi processor and camera module. The spectrometer used was the FLAME-NIR, a near-infrared spectrometer for hyperspectral measurements. RGB images and spectrometer data were captured simultaneously. As spectrometer data do not provide continuous terrain coverage, the locations of their ground elliptical footprints were determined from the bundle adjustment solution of the captured images. For each of the spectrometer ground ellipses, the land cover signature at the footprint location was determined to enable the characterization, identification, and classification of land cover elements. To attain a continuous land cover classification map, spatial interpolation was carried out from the irregularly distributed labeled spectrometer points. The accuracy of the classification was assessed using spatial intersection with the object-based image classification performed using the RGB images. Results show that in homogeneous land cover, like water, the accuracy of classification is 78% and in mixed classes, like grass, trees and manmade features, the average accuracy is 50%, thus, indicating the contribution of hyperspectral measurements of low altitude UAV-borne spectrometers to improve land cover classification. Full article
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Open AccessArticle Effective Exploration for MAVs Based on the Expected Information Gain
Received: 19 December 2017 / Revised: 28 February 2018 / Accepted: 3 March 2018 / Published: 6 March 2018
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Abstract
Micro aerial vehicles (MAVs) are an excellent platform for autonomous exploration. Most MAVs rely mainly on cameras for buliding a map of the 3D environment. Therefore, vision-based MAVs require an efficient exploration algorithm to select viewpoints that provide informative measurements. In this paper,
[...] Read more.
Micro aerial vehicles (MAVs) are an excellent platform for autonomous exploration. Most MAVs rely mainly on cameras for buliding a map of the 3D environment. Therefore, vision-based MAVs require an efficient exploration algorithm to select viewpoints that provide informative measurements. In this paper, we propose an exploration approach that selects in real time the next-best-view that maximizes the expected information gain of new measurements. In addition, we take into account the cost of reaching a new viewpoint in terms of distance and predictability of the flight path for a human observer. Finally, our approach selects a path that reduces the risk of crashes when the expected battery life comes to an end, while still maximizing the information gain in the process. We implemented and thoroughly tested our approach and the experiments show that it offers an improved performance compared to other state-of-the-art algorithms in terms of precision of the reconstruction, execution time, and smoothness of the path. Full article
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Open AccessArticle UAS Navigation with SqueezePoseNet—Accuracy Boosting for Pose Regression by Data Augmentation
Received: 20 December 2017 / Revised: 24 January 2018 / Accepted: 5 February 2018 / Published: 13 February 2018
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Abstract
The navigation of Unmanned Aerial Vehicles (UAVs) nowadays is mostly based on Global Navigation Satellite Systems (GNSSs). Drawbacks of satellite-based navigation are failures caused by occlusions or multi-path interferences. Therefore, alternative methods have been developed in recent years. Visual navigation methods such as
[...] Read more.
The navigation of Unmanned Aerial Vehicles (UAVs) nowadays is mostly based on Global Navigation Satellite Systems (GNSSs). Drawbacks of satellite-based navigation are failures caused by occlusions or multi-path interferences. Therefore, alternative methods have been developed in recent years. Visual navigation methods such as Visual Odometry (VO) or visual Simultaneous Localization and Mapping (SLAM) aid global navigation solutions by closing trajectory gaps or performing loop closures. However, if the trajectory estimation is interrupted or not available, a re-localization is mandatory. Furthermore, the latest research has shown promising results on pose regression in 6 Degrees of Freedom (DoF) based on Convolutional Neural Networks (CNNs). Additionally, existing navigation methods can benefit from these networks. In this article, a method for GNSS-free and fast image-based pose regression by utilizing a small Convolutional Neural Network is presented. Therefore, a small CNN (SqueezePoseNet) is utilized, transfer learning is applied and the network is tuned for pose regression. Furthermore, recent drawbacks are overcome by applying data augmentation on a training dataset utilizing simulated images. Experiments with small CNNs show promising results for GNSS-free and fast localization compared to larger networks. By training a CNN with an extended data set including simulated images, the accuracy on pose regression is improved up to 61.7% for position and up to 76.0% for rotation compared to training on a standard not-augmented data set. Full article
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