id
	16109
Url
	https://chloe.cnr.it/s/BiDiAr/item/16109
Resource template
	Academic Article
Resource class
	bibo:AcademicArticle
Title
	Deep Learning in Archaeological Remote Sensing: Automated Qanat Detection in the Kurdistan Region of Iraq
Creator
	Soroush, Mehrnoush
	Mehrtash, Alireza
	Khazraee, Emad
	Ur, Jason A.
Date
	2020
Language
	eng
Abstract
	In this paper, we report the results of our work on automated detection of qanat shafts on the Cold War-era CORONA Satellite Imagery. The increasing quantity of air and space-borne imagery available to archaeologists and the advances in computational science have created an emerging interest in automated archaeological detection. Traditional pattern recognition methods proved to have limited applicability for archaeological prospection, for a variety of reasons, including a high rate of false positives. Since 2012, however, a breakthrough has been made in the field of image recognition through deep learning. We have tested the application of deep convolutional neural networks (CNNs) for automated remote sensing detection of archaeological features. Our case study is the qanat systems of the Erbil Plain in the Kurdistan Region of Iraq. The signature of the underground qanat systems on the remote sensing data are the semi-circular openings of their vertical shafts. We choose to focus on qanat shafts because they are promising targets for pattern recognition and because the richness and the extent of the qanat landscapes cannot be properly captured across vast territories without automated techniques. Our project is the first effort to use automated techniques on historic satellite imagery that takes advantage of neither the spectral imagery resolution nor very high (sub-meter) spatial resolution.
Is Part Of
	Remote Sensing
Doi
	https://doi.org/10.3390/rs12030500
Issn
	2072-4292
Issue
	3
Pages
	500
Volume
	12
Homepage
	https://www.zotero.org/groups/5293298/bidiar/items/RQ35ZZZI/item-list

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