Journal
REMOTE SENSING
Volume 14, Issue 14, Pages -Publisher
MDPI
DOI: 10.3390/rs14143253
Keywords
Google Earth Engine (GEE); artificial intelligence (AI); machine learning; deep learning; computer vision; remote sensing; cloud computing; geospatial big data; review
Categories
Funding
- US National Aeronautics and Space Administration [80NSSC22K0384]
- College of Arts and Sciences at University of New Mexico
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Remote sensing plays a crucial role in various domains, with the integration of AI methods in GEE offering a promising pathway towards automated monitoring programs.
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). Retrieving, managing, and analyzing large amounts of RS imagery poses substantial challenges. Google Earth Engine (GEE) provides a scalable, cloud-based, geospatial retrieval and processing platform. 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. 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. In this article, we provide a systematic review of relevant literature to identify recent research that incorporates AI methods in GEE. We then discuss some of the major challenges of integrating GEE and AI and identify several priorities for future research. We developed an interactive web application designed to allow readers to intuitively and dynamically review the publications included in this literature review.
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