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Communication

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

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Institute of Archaeology, University College London, London WC1E 6BT, UK
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Computer Science and Information Technology, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates
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Institut für Ur- und Frühgeschichte und Vorderasiatische Archäologie, Universität Heidelberg, Sandgasse 7, 69117 Heidelberg, Germany
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Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates
*
Author to whom correspondence should be addressed.
Academic Editors: Riccardo Roncella and Deodato Tapete
Remote Sens. 2022, 14(3), 553; https://doi.org/10.3390/rs14030553
Received: 29 November 2021 / Revised: 5 January 2022 / Accepted: 21 January 2022 / Published: 24 January 2022
(This article belongs to the Section AI Remote Sensing)
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. View Full-Text
Keywords: unmanned aerial vehicles; optical; deep learning; archaeology; feature detection; software; high-performance computing unmanned aerial vehicles; optical; deep learning; archaeology; feature detection; software; high-performance computing
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  • Externally hosted supplementary file 1
    Doi: https://doi.org/10.5522/04/16685518.v2
    Link: https://rdr.ucl.ac.uk/articles/dataset/Remote_Sensing_DL_Data/16685518
    Description: The model used for segmentation can be found here: https://rdr.ucl.ac.uk/articles/dataset/Mask_RCNN_Library/16685548/1 The software code can be found here: https://github.com/maltaweel/Mask_UAV
MDPI and ACS Style

Altaweel, M.; Khelifi, A.; Li, Z.; Squitieri, A.; Basmaji, T.; Ghazal, M. Automated Archaeological Feature Detection Using Deep Learning on Optical UAV Imagery: Preliminary Results. Remote Sens. 2022, 14, 553. https://doi.org/10.3390/rs14030553

AMA Style

Altaweel M, Khelifi A, Li Z, Squitieri A, Basmaji T, Ghazal M. Automated Archaeological Feature Detection Using Deep Learning on Optical UAV Imagery: Preliminary Results. Remote Sensing. 2022; 14(3):553. https://doi.org/10.3390/rs14030553

Chicago/Turabian Style

Altaweel, Mark, Adel Khelifi, Zehao Li, Andrea Squitieri, Tasnim Basmaji, and Mohammed Ghazal. 2022. "Automated Archaeological Feature Detection Using Deep Learning on Optical UAV Imagery: Preliminary Results" Remote Sensing 14, no. 3: 553. https://doi.org/10.3390/rs14030553

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