--- id: engines/google-earth-engine about: label: Google Earth Engine description: >- Google's cloud platform for analysing satellite imagery and map data from its own catalogue, through a browser code editor or Python; free for noncommercial use within quotas, paid otherwise. tags: - satellite imagery - remote sensing - land cover - Python - JavaScript - cloud tool: form: hosted analysis platform with JavaScript and Python APIs group: Earth observation and AI homepage: 'https://earthengine.google.com/' licence: client library Apache-2.0; the service is Google's cost: >- free for noncommercial use within monthly compute quotas; commercial use billed by usage, with plans from $500 a month open: source: partly data: partly api: 'yes' participation: closed licence: Apache-2.0 note: >- The Python and JavaScript client libraries are open source; the platform that runs the analysis is not. domains: - places - ecology - water relations: - predicate: answers target: questions/land-use-change provenance: origin: agent sources: - title: Google Earth Engine url: 'https://earthengine.google.com/' - title: FAQ – Google Earth Engine url: 'https://earthengine.google.com/faq/' - title: About Google Earth Engine url: 'https://developers.google.com/earth-engine/guides' - title: Earth Engine Data Catalog url: 'https://developers.google.com/earth-engine/datasets' - title: Earth Engine Code Editor url: 'https://developers.google.com/earth-engine/guides/playground' - title: Python Installation url: 'https://developers.google.com/earth-engine/guides/python_install' - title: Noncommercial – Google Earth Engine url: 'https://earthengine.google.com/noncommercial/' - title: Earth Engine Noncommercial Tiers url: 'https://developers.google.com/earth-engine/guides/noncommercial_tiers' - title: Google Earth Engine pricing url: 'https://cloud.google.com/earth-engine/pricing' - title: google/earthengine-api (GitHub) url: 'https://github.com/google/earthengine-api' created: '2026-10-26' reviewed: '2026-10-26' reviewed_by: 'Claude, from the sources listed' status: draft --- Google Earth Engine is a Google Cloud product that pairs a large catalogue of satellite imagery and geospatial datasets with Google's computing, so an analysis runs on Google's servers instead of on the user's own machine.[^3] Google describes its users as researchers, nonprofits, educators and government agencies, who use it to detect changes, map trends and measure differences on the Earth's surface.[^1,2] The paper to cite is Gorelick and co-authors, "Google Earth Engine: Planetary-scale geospatial analysis for everyone", in Remote Sensing of Environment, 2017.[^2] The public catalogue holds more than eighty petabytes of data and is updated and expanded daily.[^1] Google's pages put its reach back in time at more than thirty years on the home page and more than forty years in the catalogue and FAQ.[^1,2,4] It includes the whole USGS and NASA Landsat archive, which has imaged the Earth since 1972 and now covers the surface at 30-metre resolution about every two weeks.[^2,4] Alongside it are daily MODIS imagery going back to 1999 and Sentinel radar and optical imagery.[^4] There is US aerial photography at one metre (NAIP) and global elevation at 30 metres (SRTM).[^4] Land cover maps include Dynamic World and ESA WorldCover.[^4] Weather and climate data run from forecasts through reanalysis to downscaled climate projections.[^4] The Satellite Embedding Dataset, built with Google's [AlphaEarth Foundations](/engines/alphaearth-prithvi/) model, packs a year of data from several sources into every 10-metre pixel.[^1] Users can upload their own raster and vector data, such as GeoTIFFs and shapefiles.[^2] There are two ways to work. The Code Editor is a web page for writing JavaScript, with a map, a console, a task list, drawing tools for marking areas, and a link button to share a script.[^5] The Python client library, `earthengine-api`, is already installed in Google Colab and otherwise installs with pip or conda; it has to be authenticated and tied to a Google Cloud project before use.[^6] Work can run interactively for quick looks or as batch jobs for large ones.[^3] Results can be exported to Google Drive or Cloud Storage, downloaded into Python's data tools, or published as small web apps.[^3] Machine-learning tools for classification, regression and image segmentation are built in.[^3] Google says the scripts a user writes and the results of the analysis belong to the user.[^2] The client libraries are open source under Apache-2.0; the processing itself happens on Google's side.[^5,10] Access starts by registering a Google Cloud project.[^2] It is free for noncommercial use by nonprofits, by students and staff at schools and universities for research or teaching, by news organisations, by trainers and trainees during a course, and by individuals.[^7] A government agency gets it free if it is in a UN Least Developed Country, is a recognised Indigenous government, or is doing scholarly research.[^7] Government work that is maintained on an ongoing basis needs a commercial licence.[^7] Free users may not use it for paid fee-for-service work.[^7] Since 27 April 2026 Google has been rolling out a monthly compute quota for each noncommercial project, counted in EECU-hours (an hour of one Earth Engine compute unit, Google's measure of processing power).[^8] The default Community tier allows 150 a month.[^8] The Contributor tier allows 1,000 and needs a billing account, though noncommercial use is not charged.[^8] The Partner tier allows 100,000 and is by application, for climate and nature work.[^8] Past the quota, work still runs but more slowly, and noncommercial status has to be reverified every year.[^8] Commercial use can be pay-as-you-go on the Limited plan, or a monthly plan: Basic at $500 and Professional at $2,000, each with some compute and storage included, and Premium priced on request.[^9] Compute beyond that costs $0.40 per EECU-hour, falling to $0.28 and $0.16 at higher monthly volumes.[^9] For someone modelling a place, Earth Engine gets the ground under the model without downloading whole archives.[^3] Land cover, elevation and decades of imagery can be clipped to a watershed or county and exported as inputs.[^3,4] Whether that is free depends on who is doing it and why: a nonprofit's or a student's noncommercial project qualifies, while a government agency running it as an ongoing service needs a commercial licence.[^7]