The Endangered Archaeology in the Middle East and North Africa Project: Origins, Development and Future Directions

Bewley, Bob; Bradbury, Jennie

doi:10.1080/17527260.2017.1556907

Cited by 4 publications

(3 citation statements)

References 2 publications

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“…Remote sensing, when combined with field research, allows for quick and high-resolution documentation of archaeological sites [20][21][22]. It makes it possible to document and monitor sites that are unreachable for fieldwork, destroyed, or threatened [23][24][25].…”

Section: Remote Sensing Deep Learning and Uav Imagery In Archaeologymentioning

confidence: 99%

Automated Archaeological Feature Detection Using Deep Learning on Optical UAV Imagery: Preliminary Results

Altaweel

Khelifi

et al. 2022

Remote Sensing

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This communication article provides a call for unmanned aerial vehicle (UAV) users in archaeology to make imagery data more publicly available while developing a new application to facilitate the use of a common deep learning algorithm (mask region-based convolutional neural network; Mask R-CNN) for instance segmentation. The intent is to provide specialists with a GUI-based tool that can apply annotation used for training for neural network models, enable training and development of segmentation models, and allow classification of imagery data to facilitate auto-discovery of features. The tool is generic and can be used for a variety of settings, although the tool was tested using datasets from the United Arab Emirates (UAE), Oman, Iran, Iraq, and Jordan. Current outputs suggest that trained data are able to help identify ruined structures, that is, structures such as burials, exposed building ruins, and other surface features that are in some degraded state. Additionally, qanat(s), or ancient underground channels having surface access holes, and mounded sites, which have distinctive hill-shaped features, are also identified. Other classes are also possible, and the tool helps users make their own training-based approach and feature identification classes. To improve accuracy, we strongly urge greater publication of UAV imagery data by projects using open journal publications and public repositories. This is something done in other fields with UAV data and is now needed in heritage and archaeology. Our tool is provided as part of the outputs given.

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Section: Remote Sensing Deep Learning and Uav Imagery In Archaeologymentioning

confidence: 99%

Automated Archaeological Feature Detection Using Deep Learning on Optical UAV Imagery: Preliminary Results

Altaweel

Khelifi

et al. 2022

Remote Sensing

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“…Combined with field research, remote sensing permits rapid and high-resolution documentation of the ancient landscapes [1][2][3][4]. It also 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.…”

Section: Introductionmentioning

confidence: 99%

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

Soroush

Mehrtash

Khazraee³

et al. 2020

Remote Sensing

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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.

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“…Remote sensing, when combined with field research, allows for quick and high-resolution documentation of old sites [29][30][31]. It also makes it possible to document and monitor old sites that are unreachable to fieldwork, destroyed, or threatened [32,33]. Archaeologists have been experimenting with computational approaches in remote sensing-based archaeological prospection since the mid-2000s [34,35].…”

mentioning

confidence: 99%

Autonomous Service Drones for Multimodal Detection and Monitoring of Archaeological Sites

et al. 2021

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Constant detection and monitoring of archaeological sites and objects have always been an important national goal for many countries. The early identification of changes is crucial to preventive conservation. Archaeologists have always considered using service drones to automate collecting data on and below the ground surface of archaeological sites, with cost and technical barriers being the main hurdles against the wide-scale deployment. Advances in thermal imaging, depth imaging, drones, and artificial intelligence have driven the cost down and improved the quality and volume of data collected and processed. This paper proposes an end-to-end framework for archaeological sites detection and monitoring using autonomous service drones. We mount RGB, depth, and thermal cameras on an autonomous drone for low-altitude data acquisition. To align and aggregate collected images, we propose two-stage multimodal depth-to-RGB and thermal-to-RGB mosaicking algorithms. We then apply detection algorithms to the stitched images to identify change regions and design a user interface to monitor these regions over time. Our results show we can create overlays of aligned thermal and depth data on RGB mosaics of archaeological sites. We tested our change detection algorithm and found it has a root mean square error of 0.04. To validate the proposed framework, we tested our thermal image stitching pipeline against state-of-the-art commercial software. We cost-effectively replicated its functionality while adding a new depth-based modality and created a user interface for temporally monitoring changes in multimodal views of archaeological sites.

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The Endangered Archaeology in the Middle East and North Africa Project: Origins, Development and Future Directions

Cited by 4 publications

References 2 publications

Automated Archaeological Feature Detection Using Deep Learning on Optical UAV Imagery: Preliminary Results

Automated Archaeological Feature Detection Using Deep Learning on Optical UAV Imagery: Preliminary Results

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

Autonomous Service Drones for Multimodal Detection and Monitoring of Archaeological Sites

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