The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Review of Unmanned Aerial System (UAS) applications in the built environment: Towards automated building inspection procedures using drones
Abstract
Abstract Unmanned Aerial Systems (UAS – a.k.a. drones) have evolved over the past decade as both advanced military technology and off-the-shelf consumer devices. There is a gradual shift towards public use of drones, which presents opportunities for effective remote procedures that can disrupt a variety of built environment disciplines. UAS equipment with remote sensing gear present an opportunity for analysis and inspection of existing building stocks, where architects, engineers, building energy auditors as well as owners can document building performance, visualize heat transfer using infrared imaging and create digital models using 3D photogrammetry. This paper presents a comprehensive review of various literature that addresses this topic, followed by the identification of a standard procedures for operating a UAS for energy audit missions. The presented framework is then tested on the Syracuse University campus site based on the literature review to showcase: 1) pre-flight inspection procedure parameters and methodologies; 2) during-flight visually identified areas of thermal anomalies using a UAS equipped with Infrared (IR) cameras and; 3) 3D CAD modeling developed through data gathered using UAS. A discussion of the findings suggests refining procedure accuracy through further empirical experimentation, as well as study replication, as a step towards standardizing the automation of building envelope inspection.
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Citations
Cites
UAV for 3D mapping applications: a review
Nex, F., & Remondino, F. (2014) · Applied Geomatics · doi:10.1007/s12518-013-0120-x · Cited by 1,878
Mobile 3D mapping for surveying earthwork projects using an Unmanned Aerial Vehicle (UAV) system
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UAV PHOTOGRAMMETRY FOR MAPPING AND 3D MODELING – CURRENT STATUS AND FUTURE PERSPECTIVES
Remondino, F., Barazzetti, L., Nex, F., Scaioni, M., & Sarazzi, D. (2012) · The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences · doi:10.5194/isprsarchives-xxxviii-1-c22-25-2011 · Cited by 702
Infrared thermography (IRT) applications for building diagnostics: A review
Kylili, A., Fokaides, P. A., Christou, P., & Kalogirou, S. A. (2014) · Applied Energy · doi:10.1016/j.apenergy.2014.08.005 · Cited by 526
Application of infrared thermography to the non-destructive testing of concrete and masonry bridges
Clark, M. R., McCann, D. M., & Forde, M. C. (2003) · NDT & E International · doi:10.1016/s0963-8695(02)00060-9 · Cited by 389
Infrared Thermal Imaging: Fundamentals, Research and Applications
Vollmer, M., & Möllmann, K. (2017) · Wiley eBooks · doi:10.1002/9783527693306 · Cited by 317
The societal impact of commercial drones
Rao, B., Gopi, A. G., & Maione, R. (2016) · Technology in Society · doi:10.1016/j.techsoc.2016.02.009 · Cited by 317
Quality Assessment of Unmanned Aerial Vehicle (UAV) Based Visual Inspection of Structures
Morgenthal, G., & Hallermann, N. (2014) · Advances in Structural Engineering · doi:10.1260/1369-4332.17.3.289 · Cited by 267
Evaluation of building materials using infrared thermography
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Contour Detection in Unstructured 3D Point Clouds
Hackel, T., Wegner, J. D., & Schindler, K. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.178 · Cited by 221
Cited by
Azimi, M., Eslamlou, A., & Pekcan, G. (2020) · Sensors · doi:10.3390/s20102778 · Cited by 769
Davila Delgado, J. M., Oyedele, L., Ajayi, A., Akanbi, L., Akinade, O., Bilal, M., & Owolabi, H. (2019) · Journal of Building Engineering · doi:10.1016/j.jobe.2019.100868 · Cited by 607
Craveiro, F., Duarte, J. P., Bartolo, H., & Bartolo, P. J. (2019) · Automation in Construction · doi:10.1016/j.autcon.2019.03.011 · Cited by 455
Generative AI in the Construction Industry: A State-of-the-art Analysis
Taiwo, R., Bello, I. T., Abdulai, S. F., Yussif, A.-M., Salami, B. A., Saka, A., & Zayed, T. (2024) · arXiv · doi:10.48550/arxiv.2402.09939 · Cited by 21
Metadata
- Authors
- Rakha, T., & Gorodetsky, A.
- Venue
- Automation in Construction
- Publisher
- Elsevier BV
- Published
- 2018-09
- Volume
- 93
- Pages
- 252-264
- ISSN
- 0926-5805 (ISSN-L)
- OpenAlex
- W2803119246
- Cited by
- 470
- References
- 33
- FWCI
- 39.85
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.