Satellite altimetry technology has unparalleled advantages in the monitoring of hydrological resources. (Note that a freely accessible interactive version of the map and charts can be accessed via our web app tool the web app tool URL and its brief demo video are provided in Appendix A also, in Appendix B, we provide our resources for an introduction of a list of commonly used evaluation metrics). From Figure 5, we see that the most-used ML models are RF, SVM, and CART, while the top evaluation metrics used are: overall accuracy (OA), producer's accuracy (PA), user's accuracy (UA), and Kappa. The most-used RS data types are Landsat 8 OLI, and Sentinel-2. In Figure 4, the primary applications that have applied GEE integrated with AI are crop, LULC, vegetation, wetland, water, and forest, and that primary study areas are China, Brazil, and the United States. To break this down further, in (b) we can see that ML is the dominant method, and in (c) the most-used tasks are classification. Shows that most published work leveraging the power of GEE integrated with AI is still at the application stage and that there is room to develop novel methods to advance earth observation in relevant fields. We developed an interactive web application designed to allow readers to intuitively and dynamically review the publications included in this literature review. 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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