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.

Wind and current effects on the evolution of a two-dimensional dispersive focusing wave group are investigated using a two-phase flow model. A Navier–Stokes solver is combined with the Smagorinsky subgrid-scale stress model and volume of fluid (VOF) air–water interface capturing scheme. Model predictions compare well with the experimental data with and without wind. It was found that the following and opposing winds shift the focus point downstream and upstream, respectively. The shift of focus point is mainly due to the action of wind-driven current instead of direct wind forcing. Under strong following/opposing wind forcing, there appears a slight increase/decrease of the extreme wave height at the focus point and an asymmetric/symmetric behavior in the wave focusing and defocusing processes. Under a weak following wind, however, the extreme wave height decreases with increasing wind speed because of the dominant effect of the wind-driven current over direct wind forcing. The vertical shear of the wind-driven current plays an important role in determining the location of and the extreme wave height at the focus point under wind actions. Furthermore, it was found that the thin surface layer current is a better representation of the wind-driven current for its role in wind influences on waves than the depth-uniform current used by previous studies. Airflow structure above a breaking wave group and its link to the energy flux from wind to wave as well as wind influence on breaking are also examined. The flow structure in the presence of a following wind is similar to that over a backward-facing step, while that in the presence of an opposing wind is similar to that over an airfoil at high angles of attack. Both primary and secondary vortices are observed over the breaking wave with and without wind of either direction. Airflow separates over the steep crest and causes a pressure drop in the lee of the crest. The resulting form drag may directly affect the extreme wave height. The wave breaking location and intensity are modified by the following and opposing wind in a different fashion.

Artificial intelligence (AI) and machine learning (ML) have been applied to solve various remote sensing problems. To fully leverage the power of AI and ML to tackle impactful remote sensing problems, it is essential to enable researchers and practitioners to understand how AI and ML models actually work and thus to improve the model performance strategically. Accurate and timely land cover maps are essential components for informed land management decision making. To address the ever-increasing need for high spatial and temporal resolution maps, this paper developed an interactive and open-source online tool, in Python, to help interpret and improve the ML models used for land cover mapping with Google Earth Engine (GEE). The tool integrates the workflow of both land cover classification and land cover change dynamics, which requires the generation of a time series of land cover maps. Three feature importance metrics are reported, including impurity-based, permutation-based, and SHAP (Shapley additive explanations) value-based feature importance. Two case studies are presented to showcase the tool’s capability and ease of use, enabling a globally accessible and free convergent application of remote sensing technologies. This tool may inspire researchers to facilitate explainable AI (XAI)-empowered remote sensing applications with GEE.