Announcement: All noncommercial projects registered to use Earth Engine before April 15, 2025 must verify noncommercial eligibility to maintain Earth Engine access.
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Google Earth Engine is a Google Cloud product for geospatial analysis at scale. It combines a multi-petabyte catalog of satellite imagery and geospatial datasets with planetary-scale computation to accelerate environmental research and applications.
Key Features
Geospatial analysis, simplified and scalable
Earth Engine integrates an extensive geospatial data catalog with distributed computing, accessible through client libraries. Users can access a wide range of satellite and environmental data, as well as incorporate their own datasets. The platform simplifies geospatial analysis by automatically handling data projection, scaling, and compositing based on user-specified parameters. Its analytical functions operate efficiently across different scales without requiring explicit data preparation steps or chunking. By managing complex data processing and computational scaling internally, Earth Engine enables users to focus on analysis rather than technical setup.
Interactive mode: For rapid real-time data exploration and visualization of small amounts of data.
Batch mode: For large-scale computationally intensive tasks on large amounts of data.
Development environments
Developers can choose between two primary development environments:
Python client library: A flexible interface to Earth Engine for integration with the broader Python ecosystem, facilitating advanced workflows, and interactive analysis in Jupyter notebooks.
JavaScript Code Editor: A dedicated web-based development environment for rapid prototyping, exploration, and Earth Engine App creation.
Visualization and results
Earth Engine supports geospatial analysis from initial prototyping to final data export. Its efficient tiling and computation system, integrated with interactive map widgets, provides rapid visualization and inspection capabilities in both the Code Editor and Python environments. This allows for immediate data exploration and iteration. When ready, users can export raster and vector results to Google Cloud Storage, BigQuery, or Google Drive, as well as download data locally in formats compatible with pandas, NumPy, and Xarray. Additionally, Earth Engine supports the creation of interactive web applications, enabling users to share their geospatial insights with a wide audience.
Machine learning
Machine learning tools for regression, classification, image segmentation, and accuracy assessment are built into Earth Engine. Once trained, models can be saved and applied repeatedly. Classical ML workflows are streamlined within Earth Engine's integrated system. For more advanced options or externally trained models, integration with Vertex AI is provided, allowing models to be brought to Earth Engine's data or enabling the construction of deep learning models and neural network-based analyses.
Access and management
Earth Engine is available for both commercial and noncommercial use. Noncommercial use is offered free of charge, while commercial use is subject to a subscription fee and compute charges. All computation and private data are associated with Google Cloud projects, providing users with control over access, resource management, and usage monitoring through the Google Cloud Console. This integration allows for centralized project management, detailed billing information, and the application of Google Cloud's robust security and compliance features. Users can take advantage of Identity and Access Management (IAM) to control permissions and can log activities and monitor resource usage with Cloud Monitoring and Cloud Logging.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-11-20 UTC."],[[["\u003cp\u003eGoogle Earth Engine is a cloud-based platform that provides petabytes of satellite imagery and geospatial datasets for environmental analysis.\u003c/p\u003e\n"],["\u003cp\u003eIt offers tools for geospatial analysis, including interactive and batch processing modes, as well as Python and JavaScript development environments.\u003c/p\u003e\n"],["\u003cp\u003eUsers can visualize and export results to various formats and platforms, including Google Cloud Storage, BigQuery, and Google Drive.\u003c/p\u003e\n"],["\u003cp\u003eEarth Engine incorporates machine learning capabilities for tasks like regression, classification, and image segmentation, and integrates with Vertex AI for advanced modeling.\u003c/p\u003e\n"],["\u003cp\u003eAccess is available for both commercial and non-commercial use, with options for managing projects, resources, and permissions through Google Cloud.\u003c/p\u003e\n"]]],["Google Earth Engine enables scalable geospatial analysis by combining a vast data catalog with planetary-scale computation. Users can access, process, and analyze satellite and environmental data using Python or JavaScript. It supports both interactive and batch processing for tasks. Results can be visualized, exported to various platforms (Google Cloud Storage, BigQuery, etc.), or integrated into interactive web applications. Machine learning tools are included, and Vertex AI integration is available for advanced models. Access is managed via Google Cloud projects with commercial and non-commercial options.\n"],null,["Google Earth Engine is a [Google Cloud product](https://cloud.google.com/earth-engine) for geospatial\nanalysis at scale. It combines a multi-petabyte catalog of satellite imagery and\ngeospatial datasets with planetary-scale computation to accelerate environmental\nresearch and applications.\n\nKey Features\n\nGeospatial analysis, simplified and scalable\n\nEarth Engine integrates an extensive geospatial [data\ncatalog](/earth-engine/datasets) with distributed computing, accessible through\nclient libraries. Users can access a wide range of satellite and environmental\ndata, as well as [incorporate their own datasets](/earth-engine/guides/image_upload). The platform\nsimplifies geospatial analysis by automatically handling data projection,\nscaling, and compositing based on user-specified parameters. Its [analytical\nfunctions](/earth-engine/guides/objects_methods_overview) operate efficiently across different scales without\nrequiring explicit data preparation steps or chunking. By managing complex data\nprocessing and computational scaling internally, Earth Engine enables users to\nfocus on analysis rather than technical setup.\n\nProcessing environments\n\nEarth Engine supports [two modes of analysis](/earth-engine/guides/processing_environments):\n\n- **Interactive mode**: For rapid real-time data exploration and visualization of small amounts of data.\n- **Batch mode**: For large-scale computationally intensive tasks on large amounts of data.\n\nDevelopment environments\n\nDevelopers can choose between two primary development environments:\n\n- **Python client library**: A flexible interface to Earth Engine for integration with the broader Python ecosystem, facilitating advanced workflows, and interactive analysis in Jupyter notebooks.\n- **JavaScript Code Editor**: A dedicated web-based development environment for rapid prototyping, exploration, and Earth Engine App creation.\n\nVisualization and results\n\nEarth Engine supports geospatial analysis from initial prototyping to final data\nexport. Its efficient tiling and computation system, integrated with interactive\nmap widgets, provides rapid visualization and inspection capabilities in both\nthe Code Editor and Python environments. This allows for immediate data\nexploration and iteration. When ready, users can [export](/earth-engine/guides/exporting) raster\nand vector results to Google Cloud Storage, BigQuery, or Google Drive, as well\nas download data locally in formats compatible with pandas, NumPy, and Xarray.\nAdditionally, Earth Engine supports the creation of [interactive web\napplications](/earth-engine/guides/apps), enabling users to share their geospatial insights with\na wide audience.\n\nMachine learning\n\n[Machine learning tools](/earth-engine/guides/machine-learning) for regression, classification, image\nsegmentation, and accuracy assessment are built into Earth Engine. Once trained,\nmodels can be saved and applied repeatedly. Classical ML workflows are\nstreamlined within Earth Engine's integrated system. For more advanced options\nor externally trained models, integration with [Vertex AI](https://cloud.google.com/vertex-ai/docs/start/introduction-unified-platform) is\nprovided, allowing models to be brought to Earth Engine's data or enabling the\nconstruction of deep learning models and neural network-based analyses.\n\nAccess and management\n\nEarth Engine is available for both [commercial](https://earthengine.google.com/commercial/) and\n[noncommercial](https://earthengine.google.com/noncommercial/) use. Noncommercial use is offered free of\ncharge, while commercial use is subject to a [subscription fee and compute\ncharges](https://cloud.google.com/earth-engine/pricing). All computation and private data are associated with Google\nCloud projects, providing users with control over access, resource management,\nand usage monitoring through the Google Cloud Console. This integration allows\nfor centralized project management, detailed billing information, and the\napplication of Google Cloud's robust security and compliance features. Users can\ntake advantage of Identity and Access Management (IAM) to [control\npermissions](/earth-engine/cloud/access-control) and can [log activities](/earth-engine/guides/audit_logging) and [monitor\nresource usage](/earth-engine/guides/monitoring_usage) with Cloud Monitoring and Cloud Logging."]]