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Application of Convolutional Neural Networks on Digital Terrain Models for Analyzing Spatial Relations in Archaeology

Meyer-Heß, M. Fabian, Pfeffer, Ingo, Juergens, Carsten·2022·Remote Sensing
DOI 10.3390/rs14112535
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Abstract

Archaeological research is increasingly embedding individual sites in archaeological contexts and aims at reconstructing entire historical landscapes. In doing so, it benefits from technological developments in the field of archaeological prospection over the last 20 years, including LiDAR-based Digital Terrain Models, special visualizations, and automated site detection. The latter can generate comprehensive datasets with manageable effort that are useful for answering large-scale archaeological research questions. This article presents a highly automated workflow, in which a Convolutional Neural Network is used to detect burial mounds in the proximity of remotely located hollow ways. Detected mounds are then analyzed with respect to their distribution and a possible spatial relation to hollow ways. The detection works well, produces a reasonable number of results, and achieved a precision of at least 77%. The distribution of mounds shows a clear maximum in the radius of 2000–2500 m. This supports future research such as visibility or cost path analysis.

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  • Mask R-CNN has demonstrated effectiveness in similar archaeological contexts, including detection of burial clusters in the Mongolian steppe, burials and charcoal hearth sites in Germany, and terraces in Samoa.

    “Mask R-CNN has demonstrated its effectiveness in similar archaeological contexts34, including the detection of burial clusters across the Mongolian steppe35, burials36 and charcoal hearth sites37 in Germany, as well as terraces in Samoa38.”

    From Multiscale anthropogenic feature detection in the argentinian andes: satellite machine learning, UAV, and pedestrian survey (2026) · cites this paper as [36]

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Research works printed but not linked: 21

  1. Roalkvam, 2020, Algorithmic Classification and Statistical Modelling of Coastal Settlement Patterns in Mesolithic South-Eastern Norway, J. Comput. Appl. Archaeol., 3, 288
  2. Volkmann, 2015, Climate change, environment and migration: A GIS-based study of the Roman Iron Age to the Early Middle Ages in the river Oder region, Post-Class. Archaeol., 5, 69
  3. Schmidt, J., Werther, L., and Zielhofer, C. (2018). Shaping pre-modern digital terrain models: The former topography at Charlemagne’s canal construction site. PLoS ONE, 13. · DOI
  4. Figueiredo, A., and Leite Velho, G. (2007). Using pattern recognition to search LIDAR data for archeological sites. The World Is in Your Eyes. CAA2005, Proceedings of the 33rd Computer Applications and Quantitative Methods in Archaeology Conference, Tomar, Portugal, March 2005, CAA.
  5. Schneider, 2015, A Template-matching Approach Combining Morphometric Variables for Automated Mapping of Charcoal Kiln Sites, Archaeol. Prospect., 22, 45, 10.1002/arp.1497 · DOI
  6. Trier, 2015, Automatic detection of mound structures in airborne laser scanning data, J. Archaeol. Sci. Rep., 2, 69
  7. Freeland, 2016, Automated feature extraction for prospection and analysis of monumental earthworks from aerial LiDAR in the Kingdom of Tonga, J. Archaeol. Sci., 69, 64, 10.1016/j.jas.2016.04.011 · DOI
  8. 2017, An approach to the automatic surveying of prehistoric barrows through LiDAR, Quat. Int., 435, 135, 10.1016/j.quaint.2015.12.099 · DOI
  9. Sevara, 2016, Pixel versus object—A comparison of strategies for the semi-automated mapping of archaeological features using airborne laser scanning data, J. Archaeol. Sci. Rep., 5, 485
  10. Meyer, M.F., Pfeffer, I., and Jürgens, C. (2019). Automated Detection of Field Monuments in Digital Terrain Models of Westphalia Using OBIA. Geosciences, 9. · DOI
  11. Bonhage, 2021, A modified Mask region-based convolutional neural network approach for the automated detection of archaeological sites on high-resolution light detection and ranging-derived digital elevation models in the North German Lowland, Archaeol. Prospect., 28, 177, 10.1002/arp.1806 · DOI · Paper page
  12. Zingman, 2016, Detection of Fragmented Rectangular Enclosures in Very High Resolution Remote Sensing Images, IEEE Trans. Geosci. Remote Sens., 54, 4580, 10.1109/TGRS.2016.2545919 · DOI
  13. Davis, 2021, Deep learning reveals extent of Archaic Native American shell-ring building practices, J. Archaeol. Sci., 132, 105433, 10.1016/j.jas.2021.105433 · DOI
  14. Davis, D.S., and Lundin, J. (2021). Locating Charcoal Production Sites in Sweden Using LiDAR, Hydrological Algorithms, and Deep Learning. Remote Sens., 13. · DOI
  15. Sharma, P., and Bianchi, F.M. (2017). Large-Scale Mapping of Small Roads in Lidar Images Using Deep Convolutional Neural Networks. Image Analysis, Springer International Publishing.
  16. Lambers, 2021, Applying automated object detection in archaeological practice: A case study from the southern Netherlands, Archaeol. Prospect., 29, 15
  17. Trier, 2019, Using deep neural networks on airborne laser scanning data: Results from a case study of semi-automatic mapping of archaeological topography on Arran, Scotland, Archaeol. Prospect., 26, 165, 10.1002/arp.1731 · DOI
  18. Caspari, 2019, Convolutional neural networks for archaeological site detection—Finding “princely” tombs, J. Archaeol. Sci., 110, 104998, 10.1016/j.jas.2019.104998 · DOI
  19. Verschoof-van der Vaart, W.B., Lambers, K., Kowalczyk, W., and Bourgeois, Q.P. (2020). Combining Deep Learning and Location-Based Ranking for Large-Scale Archaeological Prospection of LiDAR Data from The Netherlands. ISPRS Int. J. Geo-Inf., 9. · DOI · Paper page
  20. 2020, Identification of Archaeologically Relevant Areas Using Open Geodata, KN J. Cartogr. Geogr. Inf., 70, 107, 10.1007/s42489-020-00049-w · DOI
  21. Hesse, 2010, LiDAR-derived Local Relief Models—A new tool for archaeological prospection, Archaeol. Prospect., 79, 67, 10.1002/arp.374 · DOI

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Metadata

Authors
Meyer-Heß, M. F., Pfeffer, I., & Juergens, C.
Venue
Remote Sensing
Publisher
MDPI AG
Published
2022-05-25
Volume
14
Issue
11
Pages
2535
ISSN
2072-4292
DOI
10.3390/rs14112535
Cited by
6
References
2
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (vor)

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