Engineering and Environmental Sciences
Przegląd Naukowy Inżynieria i Kształtowanie Środowiska
Detecting abuses in archaeological areas using k-mean clustering analysis and UAVs/drones data
QUBAA, A. (0000-0001-6805-2200), AL-HAMDANI, S. (0000-0003-4346-0587)
unmanned aerial vehicles, k-mean clustering, unsupervised classification, Pix4D, remote sensing, archaeological survey
Unmanned aerial vehicles (UAVs) or drones have made great progress in aerial surveys to research and discover heritage sites and archaeological areas, particularly after having developed their technical capabilities to carry various sensors onboard, whether they are conventional cameras, multispectral cameras, and thermal sensors. The objective of this research is to use the drone technology and k-mean clustering algorithm for the first time in Nineveh Governorate in Iraq to reveal the extent of civil excesses and random construction, as well as the looting and theft that occur in the archaeological areas. DJI Phantom 4 Pro drone was used, in addition to using the specialized Pix4D program to process drone images and make mosaics for them. Multiple flights were performed using a drone to survey multiple locations throughout the area and compare them with satellite images during different years. Drone’s data classification was implemented using a k-means clustering algorithm. The results of the data classification for three different time periods indicated that the percentage of archaeological lands decreased from 90.31% in 2004 to 25.29% in 2018. Where the work revealed the extent of the archaeological area’s great violations. The study also emphasized the importance of directing authorities of local antiquities to ensure the use of drone’s technology to obtain statistical and methodological reports periodically to assess archaeological damage and to avoid overtaking, stolen and looted of these sites.
APA style references: Qubaa, A. & Al-Hamdani, S. (2021). Detecting abuses in archaeological areas using k-mean clustering analysis and UAVs/drones data. Scientific Review Engineering and Environmental Sciences, 30 (1), 182-194. doi: 10.22630/PNIKS.2021.30.1.16
APA style citation in text: The first time citation in text: (Qubaa & Al-Hamdani, 2021), next citation: (Qubaa & Al-Hamdani, 2021)
Chicago style references: Qubaa, Abdalrahman, Saja Al-Hamdani. "Detecting abuses in archaeological areas using k-mean clustering analysis and UAVs/drones data." Scientific Review Engineering and Environmental Sciences, 91 ser., vol. 30, no 1, (2021): 182-194. doi: 10.22630/PNIKS.2021.30.1.16.
Chicago style citation in text: (Qubaa, and Al-Hamdani 2021)
Bibtxt style references: Bibtxt_utf8_91art16en.bib
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