Chapter
Nov 2, 2022

A Critical Assessment of Unmanned Aerial System Usage and Data Analysis in Forensic Assessment

Publication: Forensic Engineering 2022

ABSTRACT

In forensic assessment, new technologies are being developed and implemented at a rate which far outpaces typical updates to design standards. The speed of this technological development and implementation may prove challenging for the field of civil engineering, which relies heavily on considerations from standard of practice, because there must be a strong technical understanding of the precision, bias, and repeatability of a new assessment tool before it may be successfully used for forensic assessment. This paper presents a discussion on unmanned aerial systems (UAS), which are the leading platform for advances in technologies, such as near-infrared thermography, image processing, and machine learning. Advances in these areas are reviewed in this report. While UASs were found to be useful for certain nondestructive forensic assessments, several shortfalls, and common misunderstandings associated with these potential forensic tools were identified and discussed in this report.

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REFERENCES

ASTM. (2016). New ASTM Standards Aim to Help with Building Facade Inspections, Including Drone Use. News Release, January 1, 2016. Online at www.astm.org.
Acharya, T., and Ray, A. K. (2005). Image processing: principles and applications. John Wiley & Sons.
Ahammer, H., DeVaney, T. T., and Tritthart, H. A. (2003). “How Much Resolution is Enough? Influence of Downscaling the Pixel Resolution of Digital Images on the Generalised Dimensions,” Phys. D, Vol. 181, Nos. 3–4, pp. 147–156.
Baveye, P., Boast, C. W., Ogawa, S., Parlange, J.-Y., and Steenhuis, T. (1998). “Influence of Image Resolution and Thresholding on the Apparent Mass Fractal Characteristics of Preferential Flow Patterns in Field Soils,” Water Resour. Res., Vol. 34, No. 11, pp. 2783–2796.
Beauregard, M. S., and Mayercsik, N. P. (2019). “Airfield Maintenance Applications for Remote Sensing of Pavement Condition,” Geo-structural Aspects of Pavements, Railways, and Airfields (GAP) Conference, Colorado Springs, CO, 4-7 Nov 2019.
Beckingham, L. E., Peters, C. A., Um, W., Jones, K. W., and Lindquist, W. B. (2013). “2D and 3D Imaging Resolution Trade-Offs in Quantifying Pore Throats for Prediction of Permeability,” Adv. Water Resour., Vol. 62A, pp. 1–12.
Cai, C., Carter, B., Srivastava, M., Tsung, J., Vahedi-Faridi, J., and Wiley, C. (2016). “Designing a radiation sensing UAV system.” In 2016 IEEE Systems and Information Engineering Design Symposium (SIEDS) (pp. 165–169). IEEE.
Dearnley, R. (1985). “Effects of Resolution on the Measurement of Grain ‘Size’,” Mineralog. Mag., Vol. 49, pp. 539–546.
Cappelletti, C., Boniardi, M., Casaroli, A., De Gaetani, C. I., Passoni, D., and Pinto, L. (2019). Forensic Engineering Surveys With UAV Photogrammetry and Laser Scanning Techniques.
Entrop, A. G., and Vasenev, A. (2017). Infrared drones in the construction industry: designing a protocol for building thermography procedures. Energy procedia, 132, 63–68.
Furukawa, K., Okutani, K., Nagira, K., Otsuka, T., Itoyama, K., Nakadai, K., and Okuno, H. G. (2013). Noise correlation matrix estimation for improving sound source localization by multirotor UAV. In 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 3943–3948). IEEE.
Haag Engineering Co. Haag Certified Inspector – Residential Roofs Course Workbook. (2018)., pp. 30-31.
Kianka, K. (2015). Ready to utilize drones on the job? Factors to consider in commercial use. Hot Topics: Haag Engineering Company, July 2015.
Krings, T., Gerilowski, K., Buchwitz, M., Hartmann, J., Sachs, T., Erzinger, J., and Bovensmann, H. (2013). Quantification of methane emission rates from coal mine ventilation shafts using airborne remote sensing data. Atmospheric Measurement Techniques, 6(1), 151–166.
Kwasniak, A. (2017). Drones in Transportation Engineering: A discussion of current drone rules, equipment, and applications. Institute of Transportation Engineers. ITE Journal, 87(2), 40.
Mandelbrot, B. (1967). “How Long is the Coast of Britain? Statistical Self-Similarity and the Fractional Dimension,” Science, Vol. 156, No. 3775, pp. 636–638.
Mayercsik, N. P., Brisard, S., Vandamme, M., and Kurtis, K. E. “Using Fractal Geometry to Recover the 3D Air Void, Scale-Independent, Microstructure Information From 2D Sections of Mortars,” Advances in Civil Engineering Materials, Vol. 5, No. 2, 2016, pp. 1–21.
Padró, J. C., Muñoz, F. J., Planas, J., and Pons, X. (2019). Comparison of four UAV georeferencing methods for environmental monitoring purposes focusing on the combined use with airborne and satellite remote sensing platforms. International journal of applied earth observation and geoinformation, 75, 130–140.
Paumgartner, D., Losa, G., and Weibel, E. R. (1981). “Resolution Effect on the Stereological Estimation of Surface and Volume and its Interpretation in Terms of Fractal Dimensions,” J. Microsc., Vol. 121, No. 1, pp. 51–63.
Rakha, T., Liberty, A., Gorodetsky, A., Kakillioglu, B., and Velipasalar, S. (2018). Heat mapping drones: an autonomous computer-vision-based procedure for building envelope inspection using unmanned aerial systems (UAS). Technology| Architecture+ Design, 2(1), 30–44.
Ravich, T. M. (2015). Courts in the drone age. N. Ky. L. Rev., 42, 161.
Rigaut, J. P., Schoe¨vae¨rt-Brossault, D., Downs, A. M., and Landini, G. (1998). “Asymptotic Fractals in the Context of Grey-Scale Images,” J. Microsc., Vol. 189, No. 1 pp. 57–63.
Taddia, Y., Stecchi, F., and Pellegrinelli, A. (2019). “Using DJI Phantom 4 TK Drone for Topographic Mapping of Coastal Areas.” International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences.
Vautherin, J., Rutishauser, S., Schneider-Zapp, K., Choi, H. F., Chovancova, V., Glass, A., and Strecha, C. (2016). Photogrammetric accuracy and modeling of rolling shutter cameras. ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, 3(3).
Villa, T. F., Salimi, F., Morton, K., Morawska, L., and Gonzalez, F. (2016). “Development and validation of a UAV based system for air pollution measurements.” Sensors, 16(12), 2202.
Fonseca, L. M. G., Namikawa, L. M., and Castejon, E. F. (2009). Digital image processing in remote sensing. In 2009 Tutorials of the XXII Brazilian Symposium on Computer Graphics and Image Processing (pp. 59–71). IEEE.
Woods, R. E., and González, R. C. (2009). Digital image processing using Matlab. Gonzalez, Woods, & Eddins en: F. Giraldo; M. Gonzales; E. Camargo.(2011). Algoritmos de procesamiento de imagen satelitales con transformada Hough. Revista Visión Electrónica.
Jordan, M. I., and Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260.
Russell, S. J. (2010). Artificial intelligence: a modern approach. Prentice Hall, Upper Saddle River, N.J.
Kim, S. E., and Seo, I. (2015). Artificial Neural Network ensemble modeling with conjunctive data clustering for water quality prediction in rivers. Journal of Hydro-environment Research. 9.
O’Shea, K., and Nash, R. (2015). An introduction to convolutional neural networks. arXiv preprint arXiv:1511.08458.

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Forensic Engineering 2022
Pages: 537 - 550

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Published online: Nov 2, 2022

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Melissa Stewart Beauregard, Ph.D. [email protected]
P.E.
Nathan Paul Mayercsik, Ph.D.
P.E.
Randall Alan Pietersen
32nd Lt., Massachusetts Institute of Technology

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