Testing Google Earth Engine for the automatic identification and vectorization of archaeological features: A case study from Faynan, Jordan
Liss, Brady Howland, Matthew D. Levy, Thomas E.
Liss, Brady
Howland, Matthew D.
Levy, Thomas E.
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2017-10
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Article
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Keywords
Google Earth Engine,Remote sensing,Satellite imagery,Image classification,Digitization,Vectorization,Faynan
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Citation
Liss, B., Howland, M.D., & Levy, T.E. (2017). Testing Google Earth Engine for the automatic identification and vectorization of archaeological features: A case study from Faynan, Jordan. Journal of Archaeological Science: Reports, 15, 299-304. https://doi.org/10.1016/j.jasrep.2017.08.013
Abstract
Google Earth Engine (GEE) is an in-development, cloud-based platform providing access to petabytes of satellite imagery data for planetary-scale analysis (Google Earth Engine Team 2015). Combining this massive database with the parallel computing power of Google's infrastructure facilitates quick and easy analysis of satellite imagery on any scale, opening new avenues for research in a number of fields. This paper evaluates the potential role GEE can play in the future of archaeological research. To do so, GEE was employed/tested in two case studies. First, GEE was used to automatically identify specific archaeological features across the landscape of the archaeologically-rich Faynan region of Southern Jordan. Second, GEE-based edge-detection and automatic vectorization for mapping archaeological sites was tested at the Iron Age (ca. 1200–900 BCE) site of Khirbat al-Jariya in Faynan. Based on the test results, the authors concluded that GEE has significant potential for assisting archaeologists with automated feature detection and vectorization, tasks that are often onerous and expensive.
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Elsevier Ltd.
Journal
Journal of Archaeological Science: Reports
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2352-409X
2352-4103
2352-4103