Convolutional neural networks for archaeological site detection – Finding “princely” tombs

Caspari, Gino; Crespo, Pablo (2019). Convolutional neural networks for archaeological site detection – Finding “princely” tombs. Journal of archaeological science, 110, p. 104998. Elsevier 10.1016/j.jas.2019.104998

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Creating a quantitative overview over the early Iron Age heritage of the Eurasian steppes is a difficult task due to the vastness of the ecological zone and the often problematic access. Remote sensing based detection on open-source high-resolution satellite data in combination with convolutional neural networks (CNN) provide a potential solution to this problem. We create a CNN trained to detect early Iron Age burial mounds in freely available optical satellite data. The CNN provides a superior method for archaeological site detection based on the comparison to other detection algorithms trained on the same dataset. Throughout all comparison metrics (precision, recall, and score) the CNN performs best.

Item Type:

Journal Article (Original Article)

Division/Institute:

06 Faculty of Humanities > Department of History and Archaeology > Institute of Archaeological Sciences

UniBE Contributor:

Caspari, Gino

Subjects:

000 Computer science, knowledge & systems
600 Technology > 620 Engineering
900 History > 930 History of ancient world (to ca. 499)

ISSN:

0305-4403

Publisher:

Elsevier

Language:

English

Submitter:

Gino Caspari

Date Deposited:

19 Sep 2019 14:02

Last Modified:

24 Oct 2019 11:39

Publisher DOI:

10.1016/j.jas.2019.104998

BORIS DOI:

10.7892/boris.133265

URI:

https://boris.unibe.ch/id/eprint/133265

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