Along with this exponential increase in geospatial big data, the need for cloud computing and high-performance computing for modeling, analyzing, and simulating geospatial contents is also rapidly increasing. This trend will accelerate even faster as the world becomes more mobile and as unoccupied aircraft systems (UAS) and satellite imagery are acquired more often and at higher resolutions. About 25 PB of data are being generated per day at Google, a significant portion of which is spatio-temporal data.

The United Nations Initiative on Global Geospatial Information Management (UN-GGIM) estimated that 2.5 quintillion bytes of data (one quintillion bytes = 1000 petabytes (PB) 1 PB = 1000 Terabytes (TB)) are being generated every single day, a large portion of which is location-aware. The size of such data is growing rapidly, by at least 20% per year. Geospatial big data, which are collected with ubiquitous location-aware sensors that are inherently geospatial, are a significant portion of big data. We developed an interactive web application designed to allow readers to intuitively and dynamically review the publications included in this literature review.īig data approaches have been making substantial changes in science and in society at large.

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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