The Journal of Open Source Software · 2020 · 162 citations · 1 references
ComputingCloud ComputingVisualizationCloud InfrastructureData VisualizationScientific VisualizationApplication Programming InterfacesCloud Data PlatformsCloud-based AnalyticsData IntegrationApi-based Data AccessCloud Computing ResourcesCloud Computing SystemsData Catalog DesignData ExplorationEnvironmental Data CollectionFlexibilityJavascript
Google Earth Engine Its multi-petabyte data catalog and computation services are accessed via an Internet-accessible API. The API is exposed through JavaScript and Python client libraries. Google provides a browser-based IDE for the JavaScript API, and while convenient and useful for rapid data exploration and script development, it does not allow third-party package integration, relying solely on Google Maps and Google Charts for data visualization, and proprietary systems for metadata viewing and asset management. In contrast, the Python and Node.js distributions offer much flexibility for developers to integrate with third-party libraries. However, without the structure of a dedicated IDE, casual users can be left directionless and daunted. A significant gap exists between these two offerings (Google-supported JavaScript IDE and base client libraries) where convenience and flexibility meet. We propose to fill this gap with an R package that wraps the Earth Engine Python API to provide R users with a familiar interface, rapid development features, and flexibility to analyze data using open-source, third-party packages.
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Google Earth Engine: Planetary-scale geospatial analysis for everyone
Noel Gorelick, M. Hancher, Mike Dixon et al. · Remote Sensing of Environment · 2017 · 14K citations · Full text