The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Multidisciplinary digital methodologies for documentation and preservation of immovable Archaeological heritage in the Khovd River Valley, Western Mongolia
Abstract
Background: The archaeological and ethnographic heritages of Mongolia reflect a multi-millennial continuity of typically mobile-pastoral occupations across sparsely populated, environmentally diverse landscapes, but the threats of modernisation and industrialisation to those heritages are nevertheless present and substantial. The construction of the Erdeneburen Hydroelectric Dam on the Khovd River in western Mongolia is planned to submerge hundreds of archaeological features and jeopardise at least another thousand. Methods: The Mongolian Archaeology Project: Surveying the Steppes, in collaboration with the Mongolian Institute of Archaeology, integrates a variety of digital techniques including GIS (geographic information systems), Machine Learning automated site detection, drone mapping, and Structure-from-Motion LiDAR scanning to document the endangered archaeology. This paper presents the resulting dataset of archaeological features across three different impact zones associated with the dam construction and evaluates the degree of efficacy of the initial data integration strategy through informal partner feedback and self-assessment. Results: While only approximately 20% of the documented sites fall within the planned flood zone, the remaining sites will be subjected to collateral threats such as industrial and infrastructural development that will necessitate extended monitoring, both temporally and spatially. In consideration of these results, this paper argues that a ‘responsive’ mode of heritage disaster intervention can bridge the gap between ‘reactive’ and ‘proactive’ modes, but requires development of an integrated (digital) methodology. Conclusions : The paper concludes by offering a new, more interconnected ‘transmethodology’ that addresses spatiality, sub-sampling, data reuse, and community input across multiple disciplines such as cultural heritage preservation, salvage archaeology, computer vision, and community archaeology. The authors developed this ‘transmethodology’ and the resulting workflows out of a theoretical framework that considers principles of Symmetrical Archaeology, Resilience Humanitarianism, and the CARE standard for inclusive data management (Collective benefit, Authority to control, Responsibility, and Ethics).
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“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 [35]
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Cites
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
The FAIR Guiding Principles for scientific data management and stewardship
Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., et al. (2016) · Scientific Data · doi:10.1038/sdata.2016.18 · Cited by 18,423 · cited 1×
“The FAIR principles state that data should be Findable, Accessible, Interoperable, and Reusable (Wilkinson et al. 2016; Letellier et al. 2007; Quintero et al. 2020).”
BackgroundReview of Deep Learning Algorithms and Architectures
Shrestha, A., & Mahmood, A. (2019) · IEEE Access · doi:10.1109/access.2019.2912200 · Cited by 1,883 · cited 2×
“…of typically four layers that first sort pixel values (input layer), then filter pixels to identify edges (convolutional layer), then pool values (pooling layer), and finally connect all layers and assign a unique value to each object (fully-connected layer; Shrestha & Mahmood 2019: 53044).”
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Cited by
Sorted by citations
Peters, M. J. H., Jambajantsan, A., Freire, V. Z., Pey, L., Vaquer, J. M., & Roberts, P. (2026) · npj Heritage Science · doi:10.1038/s40494-026-02813-z
Self-citation
Metadata
- Authors
- Fisher, M. T., Jurkenas, D., Jambajantsan, A., Jamsranjav, B., Nasan-Ochir, E.-O., Gelegdorj, E., Chuluunbat, M., Petraglia, M., & Boivin, N.
- Venue
- F1000Research
- Publisher
- F1000 Research Ltd
- Published
- 2022-11-03
- Volume
- 11
- Pages
- 1250
- ISSN
- 2046-1402
- PMC
- PMC11387942
- Cited by
- 7
- References
- 3
- Language
- en
- Funders
- Arcadia, a Charitable Fund of Lisbet Rausing and Peter Baldwin (4381)
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