We then discuss some of the major challenges of integrating GEE and AI and identify several priorities for future research. In this article, we provide a systematic review of relevant literature to identify recent research that incorporates AI methods in GEE. Artificial intelligence (AI) methods are a critical enabling technology to automating the interpretation of RS imagery, particularly on object-based domains, so the integration of AI methods into GEE represents a promising path towards operationalizing automated RS-based monitoring programs. GEE also provides access to the vast majority of freely available, public, multi-temporal RS data and offers free cloud-based computational power for geospatial data analysis. Google Earth Engine (GEE) provides a scalable, cloud-based, geospatial retrieval and processing platform. Retrieving, managing, and analyzing large amounts of RS imagery poses substantial challenges. Remote sensing (RS) plays an important role gathering data in many critical domains (e.g., global climate change, risk assessment and vulnerability reduction of natural hazards, resilience of ecosystems, and urban planning).
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