Google Earth Engine

Analyze satellite imagery and geospatial data at planetary scale

Improve sustainability and climate resilience decision-making with Earth Engine's curated geospatial data catalog, and large-scale computing and advanced AI of Google Cloud.

Features

90+ petabytes of analysis-ready geospatial data

The Earth Engine catalog is one of the largest publicly available data catalogs, with 90+ petabytes of analysis-ready satellite imagery and 1,000+ curated geospatial datasets. It includes 50+ years of historical imagery, updated and expanded daily. Examples include open satellite imagery data, such as Landsat, MODIS and Sentinel, climate and weather data, geophysical data, including terrain, land cover, or cropland data. 

The extensive catalog offers data on the entire planet, allowing users to understand a variety of earth changes relevant to their sustainability goals.  

Powerful computation platform at scale

Google Cloud empowers everyone to run large-scale parallel processing using many thousands of computers. Combining Earth Engine’s data catalog with Google Cloud’s computational capability and data analytics tools, makes Earth Engine a revolutionary platform to analyze and visualize earth data at scale. 

Faster access, processing, and analysis of data means faster innovations, informed decisions, and viable solutions.

Code Editor

The Earth Engine Code Editor is a web-based coding environment designed to make developing complex geospatial workflows fast and easy with the following elements:

  • JavaScript Code Editor
  • Map display for visualizing geospatial datasets
  • API reference documentation (Docs tab)
  • Git-based Script Manager (Scripts tab)
  • Console output (Console tab)
  • Task Manager (Tasks tab) to handle long-running queries
  • Interactive map query (Inspector tab)
  • Search of the data archive or saved scripts
  • Geometry drawing tools

Earth Engine Python API and Colab experience (geemap)

Earth Engine Python API allows users to make use of Python tools for machine learning and analysis, including those for geospatial workloads, like Cloud Optimized GeoTiffs and GeoPandas. 

The geemap Python library is supported in Earth Engine, for visual workflows in Python, like panning, zooming, and drawing map polygons for zonal statistics.

Xarray, a well-known Python package, enables working with multidimensional arrays. Xee, its Earth Engine integration, lets users work with Earth Engine ImageCollections as Xarray datasets. 

Earth Engine and BigQuery interoperability

Using BigQuery with Earth Engine, users get the best of both worlds. Earth Engine focuses on image (raster) processing, whereas BigQuery is optimized for processing large tabular datasets. 

The `Export.table.toBigQuery()`function simplifies a number of workflows:

  • Combining Earth Engine and BigQuery data to get a more complete picture of a particular problem
  • Using BigQuery's analysis tools to extract insights from Earth Engine data
  • Sharing Earth Engine data with SQL users in a way that's accessible for them

Earth Engine Machine Learning

Earth Engine has built-in capabilities to allow users to train and use ML models for common scenarios with easy-to-use APIs. For instance, you can use a random forest algorithm to classify land in an area of interest. If you'd prefer to use a Deep Neural Network, you can also train a TensorFlow or PyTorch model, deploy it to Vertex AI and get predictions from within the Earth Engine Code Editor.

Import your own data for analysis with the Earth Engine data catalog

Users can import their own data (images and tables) and combine them with datasets from the Earth Engine data catalog to derive insights. Using the Asset Manager in Code Editor or command line interface (CLI), georeferenced raster datasets in GeoTIFF or TFRecord format and tabular data in Shapefile or CSV format can be imported to build data products, create models, and develop unique solutions to accelerate sustainability efforts.

Export your results for integration into other systems

If you're training a TensorFlow model or want to run hydrology simulations outside Earth Engine you might want to get data out of Earth Engine into another system. The Earth Engine export API does the heavy lifting and our data extraction methods help solve scaling issues and work with frameworks like Apache Beam, Spark, or Dask. Our Python client library comes bundled with client-side logic to convert between Earth Engine objects and NumPy, Pandas, and GeoPandas types.

Earth Engine Apps

For code-free, interactive visualizations, Earth Engine Apps are dynamic, shareable user interfaces for Earth Engine analyses. 

With Earth Engine Apps, developers can use simple UI elements to leverage Earth Engine's data catalog and analytical power, allowing stakeholders to interact with their data and delivering insights into the hands of decision-makers.

Cloud Score+

Cloud Score+ solves the issue of cloud cover in Sentinel-2 satellite data. It is a comprehensive QA score, powered by deep learning, which provides a per-pixel “usability” score to mask or weight observations based on overall quality.

Dynamic World

Dynamic World is a global, near-real-time Land Cover dataset at a 10m resolution, powered by machine learning. It gives unprecedented detail about land use and helps make accurate predictions and effective sustainability plans.

How It Works

Reference analysis-ready datasets in Earth Engine's petabyte-scale data catalog. Run raster and vector data analysis in parallel on thousands of machines. Iteratively test and tweak results in Code Editor, by writing custom Python code with Earth Engine's client library or by extracting results from a model hosted in Vertex AI. Share results in BigQuery or in Earth Engine Apps.

Solve for common geospatial use cases at scale

Common Uses

Sustainable sourcing

Enable global supply chain transparency and traceability

Sustainable supply chains are business critical. Earth Engine helps businesses analyze land cover and use at sourcing sites to highlight deforestation risk in their supply chains. The EC JRC global map of forest cover for 2020 is useful for this. A spatially explicit representation of forest presence/absence in 2020 at 10m resolution, this dataset corresponds to the EU Deforestation Regulation (EUDR), which will require companies to provide statements affirming goods sold or produced in the EU were not grown on land deforested after December 31, 2020.

Learn more about the EC JRC global map of forest cover

    Enable global supply chain transparency and traceability

    Sustainable supply chains are business critical. Earth Engine helps businesses analyze land cover and use at sourcing sites to highlight deforestation risk in their supply chains. The EC JRC global map of forest cover for 2020 is useful for this. A spatially explicit representation of forest presence/absence in 2020 at 10m resolution, this dataset corresponds to the EU Deforestation Regulation (EUDR), which will require companies to provide statements affirming goods sold or produced in the EU were not grown on land deforested after December 31, 2020.

    Learn more about the EC JRC global map of forest cover

      TraceMark: First-mile driven traceability for raw materials

      TraceMark, built by Google Cloud Advantage partner NGIS, uses Earth Engine to map the sourcing of raw materials and potential risk through global supply chains, providing comprehensive first-mile monitoring and end-to-end traceability insights.

      TraceMark leverages leading frameworks and provides EU Deforestation Regulation (EUDR)-specific capabilities for risk mitigation and due diligence, including data exchange and engagement with suppliers, and sustainability metrics for reporting.

      TraceMark provides multi commodity capability to address all EUDR-impacted products, including palm, coffee, cocoa, soy, and paper.

      Climate risk

      Safeguard assets against extreme climate risks, such as fires

      Disaster response agencies require precise and timely data and insights in order to monitor fires, evaluate risks, and protect assets. Datasets in Earth Engine, such as GOES MCMIP (imagery), GOES FDC (fire detection), and FIRMS (Fire Information for Resource Management System), can be analyzed to monitor fires, as well as facilitate fire modeling and risk management. Analyzing this data helps improve the efficiency of response and disaster recovery efforts, making them more effective.

      See an example of mapping wildfires with the power of satellite data

        Safeguard assets against extreme climate risks, such as fires

        Disaster response agencies require precise and timely data and insights in order to monitor fires, evaluate risks, and protect assets. Datasets in Earth Engine, such as GOES MCMIP (imagery), GOES FDC (fire detection), and FIRMS (Fire Information for Resource Management System), can be analyzed to monitor fires, as well as facilitate fire modeling and risk management. Analyzing this data helps improve the efficiency of response and disaster recovery efforts, making them more effective.

        See an example of mapping wildfires with the power of satellite data

          Cloud partners with climate risk expertise

          Climate Engine’s SpatiaFi solution links asset and geospatial data to support regulatory reporting, climate risk reductions, and sustainable finance.

          CARTO’s cloud native Location Intelligence platform helps organizations analyze climate impacts, optimize processes, and predict outcomes.

          Deloitte is building new geospatial planning solutions using Earth Engine and Google Cloud’s GenAI to help clients build sustainable communities and infrastructure, enhance operational resilience, and prepare for climate change impacts.

          Protect natural resources

          Sustainable management and conservation of natural resources

          Leveraging the Hansen global forest change dataset in Earth Engine, users can carry out analysis of forest change, quantifying forest change over time and charting yearly forest loss. Using the Forest Monitoring for Action (FORMA, Hammer et al. 2009) data from Global Forest Watch, users can filter by dates and configure alerts within specific areas of interest.

          Learn more about global forest change

          Sustainable management and conservation of natural resources

          Leveraging the Hansen global forest change dataset in Earth Engine, users can carry out analysis of forest change, quantifying forest change over time and charting yearly forest loss. Using the Forest Monitoring for Action (FORMA, Hammer et al. 2009) data from Global Forest Watch, users can filter by dates and configure alerts within specific areas of interest.

          Learn more about global forest change

          Agriculture

          Build toward a higher yield, lower impact food system with agriculture insights

          Earth Engine can be used to surface insights into crop health, water consumption, and seasonal patterns of productivity. MOD13A2.061 Terra Vegetation Indices 16-Day Global 1km can be leveraged to generate a time-series animation representing 20-year median vegetation productivity. For more informed decision-making, users can analyze datasets like MODIS land surface temperature data or ERA5 composites to calculate Growing Degree Days (GDDs) and then apply machine learning in Vertex AI to predict when crops will reach maturity or to calculate optimal timing for pest management.

          See NDVI times series animation tutorial
          • Case Study: Learn how Regrow Ag uses Earth Engine and Google Cloud

          • Story: Learn how Sayukt is changing the lives of farmers

          • Blog: Learn how Earth Engine can inform more sustainable agriculture practices

          Build toward a higher yield, lower impact food system with agriculture insights

          Earth Engine can be used to surface insights into crop health, water consumption, and seasonal patterns of productivity. MOD13A2.061 Terra Vegetation Indices 16-Day Global 1km can be leveraged to generate a time-series animation representing 20-year median vegetation productivity. For more informed decision-making, users can analyze datasets like MODIS land surface temperature data or ERA5 composites to calculate Growing Degree Days (GDDs) and then apply machine learning in Vertex AI to predict when crops will reach maturity or to calculate optimal timing for pest management.

          See NDVI times series animation tutorial
          • Case Study: Learn how Regrow Ag uses Earth Engine and Google Cloud

          • Story: Learn how Sayukt is changing the lives of farmers

          • Blog: Learn how Earth Engine can inform more sustainable agriculture practices

          Cloud partners with agriculture expertise

          • Woolpert: A partner and leading provider of state-of-the-art geospatial services. Woolpert has helped several organizations in both the public and private sector build their Earth Engine stack for geospatial intelligence, including agricultural analytics and other land management applications. 
          • NGIS: A dedicated geospatial company and Google Partner has extensive experience in partnering with leading agriculture organizations to implement Earth Engine. Using Earth Engine, the NGIS team has developed solutions across agricultural nutrition, protection, production, and analytics to operationalize petabytes of satellite imagery to deliver new insights and products. 
          • Spatial Informatics Group (SIG): It has been providing environmental decision support tools for 25 years. Expertise include: vegetation cover types identification through spectral discrimination; phenology and seasonal vegetation change analysis; crop monitoring and yield estimation.

          Environmental impact

          Gather environmental insights; detect and monitor change

          For public sector organizations and companies seeking to tackle emissions and derive insights into the drivers of degradation and effectiveness of interventions, custom analysis can be applied to Earth Engine datasets to detect environmental impacts over time. For example, using annual segmented Landsat time series data from 1984-2019 to depict lake drying in Bolivia, or combining methane data with other datasets—like land cover, forests, water, ecosystems, regional borders and more—to track methane emissions in a given area over time.

          See time series modeling tutorial

            Gather environmental insights; detect and monitor change

            For public sector organizations and companies seeking to tackle emissions and derive insights into the drivers of degradation and effectiveness of interventions, custom analysis can be applied to Earth Engine datasets to detect environmental impacts over time. For example, using annual segmented Landsat time series data from 1984-2019 to depict lake drying in Bolivia, or combining methane data with other datasets—like land cover, forests, water, ecosystems, regional borders and more—to track methane emissions in a given area over time.

            See time series modeling tutorial

              Cloud partners with expertise in environmental impact

              Deloitte’s methane emissions quantification solution—built on Google Earth Engine—is a geospatial artificial intelligence (AI) and machine learning (ML) analytics tool designed for organizations to monitor, quantify, and prioritize closure of problematic orphan wells to reduce methane emissions, protect water and air, and mitigate safety risks to improve human and environmental health.

              Pricing

              How Earth Engine pricing worksEarth Engine pricing is based on usage of Earth Engine resources (compute units and storage) and a monthly platform fee.
              Plans and usageDescriptionPrice (USD)

              Basic

              Best for organizations with small teams and small workloads. Includes 2 developer seats, 20 concurrent high-volume API requests, and up to 8 concurrent batch export tasks.

              $500

              per month

              Professional

              Best for organizations with moderate-sized teams and predictable, time-sensitive, large-scale workloads. Includes 5 developer seats, 500 concurrent high-volume API requests, and up to 20 concurrent batch export tasks.

              $2,000

              per month

              Premium

              Best for larger teams with business critical, time-sensitive, large-scale workloads. Premium plan allocations can be customized. Please contact your Google Cloud sales representative for more information.

              Contact us


              Compute (analysis)


              Earth Engine Compute Units (EECUs) consist of Earth Engine managed workers used to execute tasks. Compute pricing is charged by EECU-hour and rates vary based on the processing environment you use.

              Online EECUs

              Run computations synchronously and include the output directly in the response.

              $1.33

              perEECU-hour

              Batch EECUs 

              Run computations asynchronously and output results for later access (in Google Cloud Storage, the Earth Engine asset store, etc.).

              $0.40

              per EECU-hour

              Storage

              $0.026

              per GB-month

              Additional users

              First user free, $500 per month for each additional user*

              Learn more about Earth Engine pricing. View all pricing details

              How Earth Engine pricing works

              Earth Engine pricing is based on usage of Earth Engine resources (compute units and storage) and a monthly platform fee.

              Basic

              Description

              Best for organizations with small teams and small workloads. Includes 2 developer seats, 20 concurrent high-volume API requests, and up to 8 concurrent batch export tasks.

              Price (USD)

              $500

              per month

              Professional

              Description

              Best for organizations with moderate-sized teams and predictable, time-sensitive, large-scale workloads. Includes 5 developer seats, 500 concurrent high-volume API requests, and up to 20 concurrent batch export tasks.

              Price (USD)

              $2,000

              per month

              Premium

              Description

              Best for larger teams with business critical, time-sensitive, large-scale workloads. Premium plan allocations can be customized. Please contact your Google Cloud sales representative for more information.

              Price (USD)

              Contact us


              Compute (analysis)


              Description

              Earth Engine Compute Units (EECUs) consist of Earth Engine managed workers used to execute tasks. Compute pricing is charged by EECU-hour and rates vary based on the processing environment you use.

              Price (USD)

              Online EECUs

              Run computations synchronously and include the output directly in the response.

              Description

              $1.33

              perEECU-hour

              Batch EECUs 

              Run computations asynchronously and output results for later access (in Google Cloud Storage, the Earth Engine asset store, etc.).

              Description

              $0.40

              per EECU-hour

              Storage

              Description
              Price (USD)

              $0.026

              per GB-month

              Additional users

              Description
              Price (USD)

              First user free, $500 per month for each additional user*

              Learn more about Earth Engine pricing. View all pricing details

              Pricing calculator

              Calculate your Google Cloud costs

              Custom quote

              Connect with our sales team to get a custom quote for your organization.

              Get started with Earth Engine

              Quick start

              Quick intro

              Explore Earth Engine catalog

              Find best practices

              Take advantage of Google Cloud

              Partners & Integration

              Work with a partner that has deep Earth Engine expertise

              Earth Engine partners with geospatial expertise and scalable solutions, enhance Earth Engine's capabilities and help organizations mitigate impact, protect natural resources, and build a sustainable future.

              See all Earth Engine partners

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