Detecting abuses in archaeological areas using k-mean clustering analysis and UAVs/drones data

• Autorzy: Qubaa Abdalrahman , Al-Hamdani Saja

Identyfikatory

DOI

10.22630/PNIKS.2021.30.1.16

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

unmanned aerial vehicles; k-mean clustering; unsupervised classification; Pix4D; remote sensing; archaeological survey

• Wydawca: Wydawnictwo Szkoły Głównej Gospodarstwa Wiejskiego w Warszawie
• Czasopismo: Przegląd Naukowy Inżynieria i Kształtowanie Środowiska
• Rocznik: 2021
• Tom: Vol. 30, No. 1
• Strony: 182--194
• Opis fizyczny: Bibliogr. 16 poz., rys., wykr., zdj.

Twórcy

autor

Qubaa Abdalrahman

• University of Mosul, Nineveh Remote Sensing Center, Al-Hadbaa Street 16, 00964 Mosul, Iraq

• https://orcid.org/0000-0001-6805-2200

autor

Al-Hamdani Saja

• University of Mosul, Nineveh College of Computer & Mathematic Science, Department of Computer Science, Aljamea Street 16, 00964 Mosul, Iraq

• https://orcid.org/0000-0003-4346-0587

Bibliografia

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