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Open AccessArticle

Deep Learning in Archaeological Remote Sensing: Automated Qanat Detection in the Kurdistan Region of Iraq

1
Center for Geographic Analysis, Harvard University; Cambridge, MA 02138, USA
2
Harvard Medical School and University of British Columbia, Harvard University, Boston, MA 02115, USA
3
Indeed Inc., San Francisco, CA 94105, USA
4
Anthropology Department, Harvard University, Cambridge, MA 02138, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(3), 500; https://doi.org/10.3390/rs12030500
Received: 2 October 2019 / Revised: 26 January 2020 / Accepted: 27 January 2020 / Published: 4 February 2020
(This article belongs to the Special Issue 2nd Edition Advances in Remote Sensing for Archaeological Heritage)
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. View Full-Text
Keywords: remote sensing; archaeology; qanat; karez; deep learning; convolutional neural networks (CNNs); image segmentation; CORONA; Kurdistan Region of Iraq (KRG) remote sensing; archaeology; qanat; karez; deep learning; convolutional neural networks (CNNs); image segmentation; CORONA; Kurdistan Region of Iraq (KRG)
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MDPI and ACS Style

Soroush, M.; Mehrtash, A.; Khazraee, E.; Ur, J.A. Deep Learning in Archaeological Remote Sensing: Automated Qanat Detection in the Kurdistan Region of Iraq. Remote Sens. 2020, 12, 500. https://doi.org/10.3390/rs12030500

AMA Style

Soroush M, Mehrtash A, Khazraee E, Ur JA. Deep Learning in Archaeological Remote Sensing: Automated Qanat Detection in the Kurdistan Region of Iraq. Remote Sensing. 2020; 12(3):500. https://doi.org/10.3390/rs12030500

Chicago/Turabian Style

Soroush, Mehrnoush; Mehrtash, Alireza; Khazraee, Emad; Ur, Jason A. 2020. "Deep Learning in Archaeological Remote Sensing: Automated Qanat Detection in the Kurdistan Region of Iraq" Remote Sens. 12, no. 3: 500. https://doi.org/10.3390/rs12030500

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