![]() ![]() With this in mind, it is quite clear that porting sophisticated applications into the GEE framework can be quite challenging. For example, simple index iteration is not recommended when working with server-side objects, because the index itself is a client-side variable.Īs reported in the documentation, to iterate an image collection, we must define a certain recursive function, which cannot modify values outside of the function’s scope, among other limitations. As it has been noticed in this conference paper, the combination of server and client-side programming tends to be confusing. While this model enables massive parallelism in distributed commodity servers, it does so at the expense of introducing a complex coding style. For example, to select a certain area or to specify a range of dates from an image collection we would apply a certain ‘map’ operation, and to summarize the selected data according to various statistics we need to apply a ‘reduce’ operation. Each of these operations is applied to each image in the collection independently and can be roughly interpreted as ‘filtering’ and ‘aggregating’. The parallel programming framework chosen by Google is based on Map and Reduce operations. image collections), and on the other hand, client-side variables, which are only handled by the browser. GEE provides, on one hand, a set of objects that are handled exclusively by the server (i.e. GEE imposes a restricted programming framework. On the contrary, software developed with GEE can only be run on Google infrastructure. Software based on open-source can be run on any cloud provider (such as Microsoft’s planetary computer or Amazon Web Services), or on on-prem facilites. This is perfectly fine, but the policy is in contrast with the software produced by the Pangeo Project, which is an large-scale open-source effort for Big Data geoscience. The software developed by Google to power its infrastructure is not open source. However, as it is a closed-source product, in order to later transition into production a developer must stick with GEE. ![]() In a few words, Google Earth Engine should be considered as a rapid-prototyping tool for Geospatial applications. Importantly, it’s a closed-source platform, which is only free for non-commercial, non-production use. Indeed, as appealing as it is in certain cases, Google Earth Engine is not a proper fit for many projects. ![]() Now, with the recent announcement from Google that a commercial version of GEE will be available for governments and business, many about what sort of trade-off does GEE represent. The reason is simple: GEE allows processing massive amounts of remote sensing data directly in Google’s servers, enabling planetary-scale data analysis, free of cost. The use of Google Earth Engine (GEE) has been raising rapidly among researchers. ![]()
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