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.
Highlights
Remote sensing of air-borne and satellite imagery has become a key element of archaeological research and cultural resource management
Our goal was to test the hypothesis that deep learning can be used for automated detection of archaeological features with relatively uniform signature such as qanat shafts
Since 1998, archaeologists working in the Middle East have increasingly exploited CORONA imagery because it predates a great deal of landscape destruction by development
Summary
Remote sensing of air-borne and satellite imagery has become a key element of archaeological research and cultural resource management. Remote sensing permits rapid and high-resolution documentation of the ancient landscapes [1,2,3,4]. It allows for the documentation and monitoring of ancient landscapes that are inaccessible for fieldwork, threatened, or permanently destroyed [5,6,7,8]. The volume and variety of air and space-borne imagery is ever increasing, which is, in theory, of great potential for the field This “data deluge” is still handled by traditional methods of visual inspection and manual marking of the potential archaeological features.
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