Abstract
A wealth of remotely sensed data has accumulated over the past several decades and now constitutes an analytical resource primed for archaeological applications. To date, remotely sensed big data (RSBD) analytics in archaeology have focused on filling spatial gaps in the distribution of sites and features, characterizing environmental landscapes, and monitoring cultural heritage sites. The scientific promise of these data to expand our understanding of past human-environment interactions has not been fully realized. Limitations of data access, sufficient analytical and computational resources, and methodological awareness and education on the appropriate use of RSBD have limited the adoption and widespread use of RSBD in archaeology, despite its ubiquity in the earth sciences. Google Earth Engine (GEE) is a freely available planetary-scale cloud computing platform that addresses the perennial challenges of data access, analysis, and computing power that are particularly acute among archaeologists aiming to derive insights from RSBD. GEE lowers the barrier to entry for analyzing RSBD, expanding the potential for these data in the automated identification of archaeological features through deep learning; fieldwork planning and archaeological practice; modeling of past environments and environmental variability; and cultural heritage impact and risk assessments. In doing so, it also contributes to open science via increased access, transparency, and reproducibility.