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Remote Sensing
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Trends, Innovative Developments and Disruptive Applications in UAV Remote Sensing
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Trends, Innovative Developments and Disruptive Applications in UAV Remote Sensing

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Urban Remote Sensing".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 12438

Special Issue Editors

Prof. Dr. Francesco Nex

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
Dr. Davide Antonio Cucci

E-Mail Website
Guest Editor
Geneva School of Enomics and Management, University of Geneva, 1211 Geneva 4, Switzerland
Interests: SLAM; UAV; optimization; Kalman filter; INS; photogrammetric computer vision; navigation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

UAVs represent one of the most relevant emerging technologies in the remote sensing domain of the last two decades, becoming a valid alternative to traditional acquisition techniques in a wide range of applications. This has been possible thanks to the massive development of cross-disciplinary solutions and methods fusing elements from different domains such as remote sensing, computer science, artificial intelligence, and robotics. Despite this, additional efforts need to be done to develop more reliable, accurate and automated solutions to make UAVs a valid instrument to tackle the societal challenges of tomorrow. Autonomous exploration methods, AI-based algorithms, new sensor and data integration as well as the setting of disruptive integrated solutions of UAVs with other instruments are just few examples of research fields currently opened. 

This Special Issue aims at collecting new algorithms, methods, and solutions leveraging on UAV data collection and exploitation to tackle a wide range of applications and societal challenges. We welcome submissions that provide the community with advanced scientific solutions and innovative applications dealing with UAVs, including, but not limited to:

  • Photogrammetric data processing for innovative mapping applications;
  • Artificial intelligence algorithms using UAV data as input;
  • Autonomous navigation, exploration and mapping in outdoor and indoor environments;
  • Efficient sensor fusion to improve UAV navigation;
  • Data fusion: integration of UAV data with other heterogenous typologies of data;
  • Integration of heterogeneous data captured by UAVs;
  • Real-time onboard or cloud/remote data processing;
  • Collaborative or swarm of UAVs applied to remote sensing applications;
  • UAVs as part of IoT solutions;
  • Cutting edge solutions in precision farming, search and rescue, disaster monitoring, infrastructure and urban monitoring and mapping, and other relevant applications;
  • Other innovative UAV solutions to address the Sustainable Development Goals and societal challenges.

Prof. Dr. Francesco Nex
Dr. Davide Antonio Cucci
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. 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 2700 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

  • UAV
  • photogrammetry
  • deep learning
  • SLAM
  • real-time
  • onboard
  • autonomous exploration
  • 3D modeling
  • semantic analysis
  • sensor integration
  • multispectral
  • hyperspectral
  • change detection
  • precision farming
  • search and rescue
  • disaster monitoring
  • cadastral mapping
  • environmental monitoring
  • sustainable development goals

Published Papers (4 papers)

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Research

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19 pages, 47402 KiB  
Article
A UAV Path Planning Method for Building Surface Information Acquisition Utilizing Opposition-Based Learning Artificial Bee Colony Algorithm
by Hao Chen, Yuheng Liang and Xing Meng
Remote Sens. 2023, 15(17), 4312; https://doi.org/10.3390/rs15174312 - 01 Sep 2023
Cited by 1 | Viewed by 1039
Abstract
To obtain more building surface information with fewer images, an unmanned aerial vehicle (UAV) path planning method utilizing an opposition-based learning artificial bee colony (OABC) algorithm is proposed. To evaluate the obtained information, a target information entropy ratio model based on observation angles [...] Read more.
To obtain more building surface information with fewer images, an unmanned aerial vehicle (UAV) path planning method utilizing an opposition-based learning artificial bee colony (OABC) algorithm is proposed. To evaluate the obtained information, a target information entropy ratio model based on observation angles is proposed, considering the observation angle constraints under two conditions: whether there is an obstacle around the target or not. To efficiently find the optimal observation angles, half of the population that is lower-quality generates bit points through opposition-based learning. The algorithm searches for better individuals near the bit points when generating new solutions. Furthermore, to prevent individuals from observing targets repeatedly from similar angles, the concept of individual abandonment probability is proposed. The algorithm can adaptively abandon similar solutions based on the relative position between the individual and the population. To verify the effectiveness of the proposed method, information acquisition experiments were conducted on real residential buildings, and the results of 3D reconstruction were analyzed. The experiment results show that while model accuracy is comparable to that of the comparison method, the number of images obtained is reduced to one-fourth of the comparison method. The operation time is significantly reduced, and 3D reconstruction efficiency is remarkably improved. Full article
(This article belongs to the Special Issue Trends, Innovative Developments and Disruptive Applications in UAV Remote Sensing)
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21 pages, 919 KiB  
Article
Dynamic Order Picking Method for Multi-UAV System in Intelligent Warehouse
by Changwan Han, Hyeongjun Jeon, Junghyun Oh and Heungjae Lee
Remote Sens. 2022, 14(23), 6106; https://doi.org/10.3390/rs14236106 - 01 Dec 2022
Cited by 3 | Viewed by 1920
Abstract
For the logistics environment, multi-UAV algorithms have been studied for the purpose of order picking in warehouses. However, modern order picking adopts static order picking methods that struggle to cope with increasing volumes of goods because the algorithms receive orders for a certain [...] Read more.
For the logistics environment, multi-UAV algorithms have been studied for the purpose of order picking in warehouses. However, modern order picking adopts static order picking methods that struggle to cope with increasing volumes of goods because the algorithms receive orders for a certain period of time and pick only those orders. In this paper, by using the modified interventionist method and dynamic path planning, we aim to assign orders received in real-time to multi-UAVs in the warehouse, and to determine the order picking sequence and path of each UAV. The halting and correcting strategy is proposed to assign orders to UAVs in consideration of the similarity between the UAV’s picking list and the orders. A UAV starts picking orders by using the ant colony optimization algorithm for the orders initially assigned. For additional orders, the UAV modifies the picking sequence and UAV’s path in real time by using the k-opt-based algorithm. We evaluated the proposed method by changing the parameters in a simulation of a general warehouse layout. The results show that the proposed method not only reduces completion time compared to the previous algorithm but also reduces UAV’s travel distance and the collapsed time. Full article
(This article belongs to the Special Issue Trends, Innovative Developments and Disruptive Applications in UAV Remote Sensing)
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Review

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26 pages, 1955 KiB  
Review
Image-Based Obstacle Detection Methods for the Safe Navigation of Unmanned Vehicles: A Review
by Samira Badrloo, Masood Varshosaz, Saied Pirasteh and Jonathan Li
Remote Sens. 2022, 14(15), 3824; https://doi.org/10.3390/rs14153824 - 08 Aug 2022
Cited by 26 | Viewed by 6755
Abstract
Mobile robots lack a driver or a pilot and, thus, should be able to detect obstacles autonomously. This paper reviews various image-based obstacle detection techniques employed by unmanned vehicles such as Unmanned Surface Vehicles (USVs), Unmanned Aerial Vehicles (UAVs), and Micro Aerial Vehicles [...] Read more.
Mobile robots lack a driver or a pilot and, thus, should be able to detect obstacles autonomously. This paper reviews various image-based obstacle detection techniques employed by unmanned vehicles such as Unmanned Surface Vehicles (USVs), Unmanned Aerial Vehicles (UAVs), and Micro Aerial Vehicles (MAVs). More than 110 papers from 23 high-impact computer science journals, which were published over the past 20 years, were reviewed. The techniques were divided into monocular and stereo. The former uses a single camera, while the latter makes use of images taken by two synchronised cameras. Monocular obstacle detection methods are discussed in appearance-based, motion-based, depth-based, and expansion-based categories. Monocular obstacle detection approaches have simple, fast, and straightforward computations. Thus, they are more suited for robots like MAVs and compact UAVs, which usually are small and have limited processing power. On the other hand, stereo-based methods use pair(s) of synchronised cameras to generate a real-time 3D map from the surrounding objects to locate the obstacles. Stereo-based approaches have been classified into Inverse Perspective Mapping (IPM)-based and disparity histogram-based methods. Whether aerial or terrestrial, disparity histogram-based methods suffer from common problems: computational complexity, sensitivity to illumination changes, and the need for accurate camera calibration, especially when implemented on small robots. In addition, until recently, both monocular and stereo methods relied on conventional image processing techniques and, thus, did not meet the requirements of real-time applications. Therefore, deep learning networks have been the centre of focus in recent years to develop fast and reliable obstacle detection solutions. However, we observed that despite significant progress, deep learning techniques also face difficulties in complex and unknown environments where objects of varying types and shapes are present. The review suggests that detecting narrow and small, moving obstacles and fast obstacle detection are the most challenging problem to focus on in future studies. Full article
(This article belongs to the Special Issue Trends, Innovative Developments and Disruptive Applications in UAV Remote Sensing)
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Other

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18 pages, 3295 KiB  
Technical Note
Towards Improved Unmanned Aerial Vehicle Edge Intelligence: A Road Infrastructure Monitoring Case Study
by Sofia Tilon, Francesco Nex, George Vosselman, Irene Sevilla de la Llave and Norman Kerle
Remote Sens. 2022, 14(16), 4008; https://doi.org/10.3390/rs14164008 - 18 Aug 2022
Cited by 5 | Viewed by 1713
Abstract
Consumer-grade Unmanned Aerial Vehicles (UAVs) are poorly suited to monitor complex scenes where multiple analysis tasks need to be carried out in real-time and in parallel to fulfil time-critical requirements. Therefore, we developed an innovative UAV agnostic system that is able to carry [...] Read more.
Consumer-grade Unmanned Aerial Vehicles (UAVs) are poorly suited to monitor complex scenes where multiple analysis tasks need to be carried out in real-time and in parallel to fulfil time-critical requirements. Therefore, we developed an innovative UAV agnostic system that is able to carry out multiple road infrastructure monitoring tasks simultaneously and in real-time. The aim of the paper is to discuss the system design considerations and the performance of the processing pipeline in terms of computational strain and latency. The system was deployed on a unique typology of UAV and instantiated with realistic placeholder modules that are of importance for infrastructure inspection tasks, such as vehicle detection for traffic monitoring, scene segmentation for qualitative semantic reasoning, and 3D scene reconstruction for large-scale damage detection. The system was validated by carrying out a trial on a highway in Guadalajara, Spain. By utilizing edge computation and remote processing, the end-to-end pipeline, from image capture to information dissemination to drone operators on the ground, takes on average 2.9 s, which is sufficiently quick for road monitoring purposes. The system is dynamic and, therefore, can be extended with additional modules, while continuously accommodating developments in technologies, such as IoT or 5G. Full article
(This article belongs to the Special Issue Trends, Innovative Developments and Disruptive Applications in UAV Remote Sensing)
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