Testing Google Earth Engine for Remote Sensing in Archaeology: Case Studies from Faynan, Jordan
Satellite imagery and remote sensing have secured a place in the archaeological toolbox, but the scale of satellite derived data often results in large datasets with individual image tiles consisting of many gigabytes. Consequently, performing complex analyses on satellite data can be computationally intensive to a prohibitive degree. Google Earth Engine (GEE), an in-development, cloud-based platform for visualizing/analyzing satellite imagery, affords a solution for researchers with limited access to computational processing power (Gorelick et al. 2017). GEE is freely accessible through a web browser, but analysis is completed on Google’s cyber-infrastructure, facilitating rapid analyses on any scale (including planetary). Moreover, through the GEE platform, users can create custom scripts to suit their specific research questions and needs. This paper explores potential contributions of GEE to archaeological research through two case studies. First, GEE was used to automatically identify specific archaeological features across the Faynan region of Southern Jordan. Second, GEE-based edge-detection and automatic vectorization was tested for mapping archaeological sites at the Iron Age (ca. 1200–900 BCE) site of Khirbat al-Jariya in Faynan. Through these trials, GEE proved a viable tool for archaeological research with significant potential to supplement traditional forms of archaeological survey and mapping.
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Testing Google Earth Engine for Remote Sensing in Archaeology: Case Studies from Faynan, Jordan. Brady Liss, Matthew Howland, Thomas E. Levy. Presented at The 82nd Annual Meeting of the Society for American Archaeology, Washington, DC. 2018 ( tDAR id: 443422)
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Abstract Id(s): 21324