Archaeological Site Detection: Latest Results from a Deep Learning Based Europe Wide Hillfort Search
The increasing availability and resolution of LiDAR data are revolutionizing landscape archaeology, enabling unprecedented large-scale studies. However, the time-intensive nature of manual analysis has posed significant challenges. This research tackles the complexities of large-scale archaeological...
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| Language: | English |
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Ubiquity Press
2025-01-01
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| Series: | Journal of Computer Applications in Archaeology |
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| Online Access: | https://account.journal.caa-international.org/index.php/up-j-jcaa/article/view/178 |
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| author | Jürgen Landauer Simon Maddison Giacomo Fontana Axel G. Posluschny |
| author_facet | Jürgen Landauer Simon Maddison Giacomo Fontana Axel G. Posluschny |
| author_sort | Jürgen Landauer |
| collection | DOAJ |
| description | The increasing availability and resolution of LiDAR data are revolutionizing landscape archaeology, enabling unprecedented large-scale studies. However, the time-intensive nature of manual analysis has posed significant challenges. This research tackles the complexities of large-scale archaeological site detection by integrating Artificial Intelligence (AI) with LiDAR data, focusing on hillforts across diverse European landscapes. A semi-automated workflow employing Convolutional Neural Networks (CNNs) was developed and tested across three regions – England, Hesse (Germany), and Molise (Italy) – covering a total area of 180,000 km2. The methodology utilized the Atlas of Hillforts of Britain and Ireland to train a CNN on LiDAR datasets and tested the model’s transferability to Germany and Italy. Techniques such as pseudo-labelling and fine-tuning addressed the “Model Drift” problem, improving region-specific performance. The AI classifier achieved F1 scores ranging from 34–38%, demonstrating its adaptability to diverse landscapes, including the Mediterranean terrain of Molise and Hesse’s densely forested regions. Case studies identified new potential hillforts in England and promising candidates in Hesse and Molise, underscoring the effectiveness of the approach. While automation significantly reduces manual workload, human verification remains critical for refining AI predictions and addressing false positives. This study also applies different expert validation workflows, emphasizing their efficiency and adaptability to regional differences. By combining automated detection with expert review, the research showcases the potential for scalable, AI-assisted archaeological discovery across diverse landscapes, providing a valuable tool for academic research and heritage management. |
| format | Article |
| id | doaj-art-9fb67cc943074da9a511be530a71f91d |
| institution | Kabale University |
| issn | 2514-8362 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Ubiquity Press |
| record_format | Article |
| series | Journal of Computer Applications in Archaeology |
| spelling | doaj-art-9fb67cc943074da9a511be530a71f91d2025-02-11T05:35:40ZengUbiquity PressJournal of Computer Applications in Archaeology2514-83622025-01-018142–5842–5810.5334/jcaa.178176Archaeological Site Detection: Latest Results from a Deep Learning Based Europe Wide Hillfort SearchJürgen Landauer0https://orcid.org/0000-0002-4426-2657Simon Maddison1https://orcid.org/0000-0001-6011-5983Giacomo Fontana2https://orcid.org/0000-0001-5006-6604Axel G. Posluschny3https://orcid.org/0000-0002-2402-3456Landauer Research, LudwigsburgUniversity College LondonTexas Tech UniversityKeltenwelt am Glauberg, GlauburgThe increasing availability and resolution of LiDAR data are revolutionizing landscape archaeology, enabling unprecedented large-scale studies. However, the time-intensive nature of manual analysis has posed significant challenges. This research tackles the complexities of large-scale archaeological site detection by integrating Artificial Intelligence (AI) with LiDAR data, focusing on hillforts across diverse European landscapes. A semi-automated workflow employing Convolutional Neural Networks (CNNs) was developed and tested across three regions – England, Hesse (Germany), and Molise (Italy) – covering a total area of 180,000 km2. The methodology utilized the Atlas of Hillforts of Britain and Ireland to train a CNN on LiDAR datasets and tested the model’s transferability to Germany and Italy. Techniques such as pseudo-labelling and fine-tuning addressed the “Model Drift” problem, improving region-specific performance. The AI classifier achieved F1 scores ranging from 34–38%, demonstrating its adaptability to diverse landscapes, including the Mediterranean terrain of Molise and Hesse’s densely forested regions. Case studies identified new potential hillforts in England and promising candidates in Hesse and Molise, underscoring the effectiveness of the approach. While automation significantly reduces manual workload, human verification remains critical for refining AI predictions and addressing false positives. This study also applies different expert validation workflows, emphasizing their efficiency and adaptability to regional differences. By combining automated detection with expert review, the research showcases the potential for scalable, AI-assisted archaeological discovery across diverse landscapes, providing a valuable tool for academic research and heritage management.https://account.journal.caa-international.org/index.php/up-j-jcaa/article/view/178landscape archaeologyautomated detectionhillfortslidarmachine learning |
| spellingShingle | Jürgen Landauer Simon Maddison Giacomo Fontana Axel G. Posluschny Archaeological Site Detection: Latest Results from a Deep Learning Based Europe Wide Hillfort Search Journal of Computer Applications in Archaeology landscape archaeology automated detection hillforts lidar machine learning |
| title | Archaeological Site Detection: Latest Results from a Deep Learning Based Europe Wide Hillfort Search |
| title_full | Archaeological Site Detection: Latest Results from a Deep Learning Based Europe Wide Hillfort Search |
| title_fullStr | Archaeological Site Detection: Latest Results from a Deep Learning Based Europe Wide Hillfort Search |
| title_full_unstemmed | Archaeological Site Detection: Latest Results from a Deep Learning Based Europe Wide Hillfort Search |
| title_short | Archaeological Site Detection: Latest Results from a Deep Learning Based Europe Wide Hillfort Search |
| title_sort | archaeological site detection latest results from a deep learning based europe wide hillfort search |
| topic | landscape archaeology automated detection hillforts lidar machine learning |
| url | https://account.journal.caa-international.org/index.php/up-j-jcaa/article/view/178 |
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