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Drones
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Drones in the Built Environment: Applications in Construction, Architecture, Transportation,...
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Drones in the Built Environment: Applications in Construction, Architecture, Transportation, Urban and Regional Planning

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

Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 29060

Special Issue Editor

Dr. Fahim Ullah

E-Mail Website
Guest Editor
School of Surveying and Built Environment, University of Southern Queensland, Springfield Central, QLD 4300, Australia
Interests: construction management; digital construction; project management; digital built environment; smart cities
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

Drones are revolutionizing the operational methods of the traditional built environment. Ranging from critical infrastructure inspection to city and regional monitoring, drones and other Unmanned Aerial Vehicles (UAVs) are disrupting the built environment. Accordingly, various applications such as bridge monitoring, crack and defect detection in roads and buildings, building inspection, indoor and outdoor structural health monitoring, visual big data collection, smart city communications, health, safety and well-being monitoring, pavement inspection, airport runway monitoring, vehicle crash detection, mobile and aerial realities, vegetation, greenery coverage and fire monitoring, material delivery, disaster monitoring and recovery, construction machinery monitoring and other applications are enabling a paradigm shift in viewing the world. The advancements in drone technologies coupled with the latest disruptive tools and techniques such as Building Information Modeling (BIM), Scan to BIM, digital twins, ubiquitous computing, blockchains, Internet of Things (IoT), Artificial Intelligence (AI), machine learning, Virtual Reality (VR), Augmented Reality (AR), Extended Reality (XR), georeferenced point clouds, Geographical information systems (GIS), 3D image reconstruction, video-based motion monitoring, and others have made this possible. However, while much promise has been shown by such applications of drones in the built environment, the state of exploration is still in its nascency, and comprehensive studies are needed to enable a smart and sustainable drone-powered built environment. Accordingly, if adopted, such drone-powered technologies can help disrupt the otherwise tech-averse fields of the built environment, including construction, architecture, transportation, urban and regional planning. This Special Issue invites and aims to attract contributions from drone and UAV enthusiasts, construction managers, architects, project managers, civil engineers, city and urban planners, real estate and property managers, data scientists, IT managers, computer systems analysts, software developers, web developers, and others. Topics may include, but are not limited to, any of the following:

  • Drones in construction.
  • Drones in architecture.
  • Drones in transportation.
  • Drones in urban and regional planning.
  • Drones for structural health monitoring.
  • Drones for building inspection.
  • Drones for infrastructure inspection.
  • Drones for bridge inspection.
  • Drones for safety and well-being monitoring in the built environment.
  • Drone-based video monitoring of constructed facilities.
  • Drones for machinery and equipment management.
  • Drones in smart cities.
  • Scan to BIM.
  • Drones for digital twins.
  • Drones for virtual, augmented, and extended realities.
  • AI-powered drones in the built environment.
  • Machine learning applications for drones in the built environment.
  • Image processing and computer vision for drones in the built environment.
  • IoT-powered drones in the built environment.
  • Scanners and drones in the built environment.
  • Sensors and drones in the built environment.
  • Drones and GIS applications in the built environment.
  • Other drone and UAV technologies in the built environment.

Dr. Fahim Ullah
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 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.

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Published Papers (3 papers)

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Research

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28 pages, 5004 KiB  
Article
Disaster Region Coverage Using Drones: Maximum Area Coverage and Minimum Resource Utilisation
by Hafiz Suliman Munawar, Ahmed W.A. Hammad and S. Travis Waller
Drones 2022, 6(4), 96; https://doi.org/10.3390/drones6040096 - 13 Apr 2022
Cited by 20 | Viewed by 6298
Abstract
The purpose of this study is to develop a design for maximum area drone coverage in a post-disaster flood situation. When it comes to covering a disaster-region for monitoring and detection of the extent of damage and losses, a suitable and technically balanced [...] Read more.
The purpose of this study is to develop a design for maximum area drone coverage in a post-disaster flood situation. When it comes to covering a disaster-region for monitoring and detection of the extent of damage and losses, a suitable and technically balanced approach is vital to achieving the best solution while covering the maximum affected area. Therefore, a mathematical optimisation model is proposed to effectively capture maximum images of the impacted region. The particle swarm optimisation (PSO) algorithm is used to solve the optimisation problem. Modern relief missions heavily rely on drones, specifically in the case of flooding, to capture the damage due to the disaster and to create roadmaps to help impacted people. This system has convincing results for inertia, exploration, exploitation, velocity, and determining the height of the drones to enhance the response to a disaster. The proposed approach indicates that when maintaining the flight height of the drone above 120 m, the coverage can be enhanced by approximately 34% compared with a flight height of 100 m. Full article
(This article belongs to the Special Issue Drones in the Built Environment: Applications in Construction, Architecture, Transportation, Urban and Regional Planning)
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23 pages, 5112 KiB  
Article
Inspecting Buildings Using Drones and Computer Vision: A Machine Learning Approach to Detect Cracks and Damages
by Hafiz Suliman Munawar, Fahim Ullah, Amirhossein Heravi, Muhammad Jamaluddin Thaheem and Ahsen Maqsoom
Drones 2022, 6(1), 5; https://doi.org/10.3390/drones6010005 - 24 Dec 2021
Cited by 49 | Viewed by 13252
Abstract
Manual inspection of infrastructure damages such as building cracks is difficult due to the objectivity and reliability of assessment and high demands of time and costs. This can be automated using unmanned aerial vehicles (UAVs) for aerial imagery of damages. Numerous computer vision-based [...] Read more.
Manual inspection of infrastructure damages such as building cracks is difficult due to the objectivity and reliability of assessment and high demands of time and costs. This can be automated using unmanned aerial vehicles (UAVs) for aerial imagery of damages. Numerous computer vision-based approaches have been applied to address the limitations of crack detection but they have their limitations that can be overcome by using various hybrid approaches based on artificial intelligence (AI) and machine learning (ML) techniques. The convolutional neural networks (CNNs), an application of the deep learning (DL) method, display remarkable potential for automatically detecting image features such as damages and are less sensitive to image noise. A modified deep hierarchical CNN architecture has been used in this study for crack detection and damage assessment in civil infrastructures. The proposed architecture is based on 16 convolution layers and a cycle generative adversarial network (CycleGAN). For this study, the crack images were collected using UAVs and open-source images of mid to high rise buildings (five stories and above) constructed during 2000 in Sydney, Australia. Conventionally, a CNN network only utilizes the last layer of convolution. However, our proposed network is based on the utility of multiple layers. Another important component of the proposed CNN architecture is the application of guided filtering (GF) and conditional random fields (CRFs) to refine the predicted outputs to get reliable results. Benchmarking data (600 images) of Sydney-based buildings damages was used to test the proposed architecture. The proposed deep hierarchical CNN architecture produced superior performance when evaluated using five methods: GF method, Baseline (BN) method, Deep-Crack BN, Deep-Crack GF, and SegNet. Overall, the GF method outperformed all other methods as indicated by the global accuracy (0.990), class average accuracy (0.939), mean intersection of the union overall classes (IoU) (0.879), precision (0.838), recall (0.879), and F-score (0.8581) values. Overall, the proposed CNN architecture provides the advantages of reduced noise, highly integrated supervision of features, adequate learning, and aggregation of both multi-scale and multilevel features during the training procedure along with the refinement of the overall output predictions. Full article
(This article belongs to the Special Issue Drones in the Built Environment: Applications in Construction, Architecture, Transportation, Urban and Regional Planning)
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Review

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20 pages, 7237 KiB  
Review
Impact of UAV Hardware Options on Bridge Inspection Mission Capabilities
by Zahra Ameli, Yugandhar Aremanda, Wilhelm A. Friess and Eric N. Landis
Drones 2022, 6(3), 64; https://doi.org/10.3390/drones6030064 - 28 Feb 2022
Cited by 22 | Viewed by 7607
Abstract
Uncrewed Aerial Vehicles (UAV) constitute a rapidly evolving technology field that is becoming more accessible and capable of supplementing, expanding, and even replacing some traditionally manual bridge inspections. Given the classification of the bridge inspection types as initial, routine, in-depth, damage, special, and [...] Read more.
Uncrewed Aerial Vehicles (UAV) constitute a rapidly evolving technology field that is becoming more accessible and capable of supplementing, expanding, and even replacing some traditionally manual bridge inspections. Given the classification of the bridge inspection types as initial, routine, in-depth, damage, special, and fracture critical members, specific UAV mission requirements can be developed, and their suitability for UAV application examined. Results of a review of 23 applications of UAVs in bridge inspections indicate that mission sensor and payload needs dictate the UAV configuration and size, resulting in quadcopter configurations being most suitable for visual camera inspections (43% of visual inspections use quadcopters), and hexa- and octocopter configurations being more suitable for higher payload hyperspectral, multispectral, and Light Detection and Ranging (LiDAR) inspections (13%). In addition, the number of motors and size of the aircraft are the primary drivers in the cost of the vehicle. 75% of vehicles rely on GPS for navigation, and none of them are capable of contact inspections. Factors that limit the use of UAVs in bridge inspections include the UAV endurance, the capability of navigation in GPS deprived environments, the stability in confined spaces in close proximity to structural elements, and the cost. Current research trends in UAV technologies address some of these limitations, such as obstacle detection and avoidance methods, autonomous flight path planning and optimization, and UAV hardware optimization for specific mission requirements. Full article
(This article belongs to the Special Issue Drones in the Built Environment: Applications in Construction, Architecture, Transportation, Urban and Regional Planning)
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