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Google Earth Engine 简介
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Google Earth Engine 是一款用于大规模地理空间分析的 Google Cloud 产品。它将 PB 级的卫星图像和地理空间数据集目录与行星级计算相结合，以加快环境研究和应用的速度。
主要功能
简化且可扩缩的地理空间分析
Earth Engine 将丰富的地理空间数据目录与分布式计算集成在一起，可通过客户端库访问。用户可以访问各种卫星和环境数据，还可以纳入自己的数据集。该平台可根据用户指定的参数自动处理数据投影、缩放和合成，从而简化地理空间分析。它的分析函数可在不同规模下高效运行，而无需执行显式的数据准备步骤或分块。通过在内部管理复杂的数据处理和计算扩缩，Earth Engine 让用户能够专注于分析，而不是技术设置。
处理环境
Earth Engine 支持两种分析模式：
- 交互式模式：适用于快速实时探索和可视化少量数据。
- 批处理模式：适用于对大量数据执行计算密集型大规模任务。
开发环境
开发者可以从以下两个主要开发环境中进行选择：
- Python 客户端库：Earth Engine 的灵活接口，可与更广泛的 Python 生态系统集成，从而简化高级工作流程，并在 Jupyter 笔记本中进行互动式分析。
- JavaScript 代码编辑器：一个专用的基于 Web 的开发环境，用于快速原型设计、探索和创建 Earth Engine 应用。
可视化和结果
Earth Engine 支持从初始原型设计到最终数据导出进行地理空间分析。其高效的平铺和计算系统与交互式地图 widget 集成，可在代码编辑器和 Python 环境中提供快速可视化和检查功能。这样，您就可以立即进行数据探索和迭代。准备就绪后，用户可以将栅格和矢量结果导出到 Google Cloud Storage、BigQuery 或 Google 云端硬盘，还可以以与 pandas、NumPy 和 Xarray 兼容的格式将数据下载到本地。此外，Earth Engine 支持创建互动式 Web 应用，让用户能够与广大受众分享其地理空间数据洞见。
机器学习
Earth Engine 中内置了用于回归、分类、图像分割和准确性评估的机器学习工具。模型训练完成后，可以保存并反复应用。传统的机器学习工作流在 Earth Engine 的集成系统中得到了简化。对于更高级的选项或外部训练的模型，我们提供了与 Vertex AI 的集成，可将模型引入 Earth Engine 的数据中，或构建深度学习模型和基于神经网络的分析。
访问和管理
Earth Engine 可用于商业和非商业用途。非商业用途可免费使用，而商业用途需支付订阅费和计算费用。所有计算数据和私密数据都与 Google Cloud 项目相关联，可让用户通过 Google Cloud 控制台控制访问权限、资源管理和使用情况监控。通过这项集成，您可以集中管理项目、查看详细的结算信息，并应用 Google Cloud 强大的安全和合规性功能。用户可以利用 Identity and Access Management (IAM) 控制权限，还可以使用 Cloud Monitoring 和 Cloud Logging 记录活动和监控资源使用情况。
如未另行说明，那么本页面中的内容已根据知识共享署名 4.0 许可获得了许可，并且代码示例已根据 Apache 2.0 许可获得了许可。有关详情，请参阅 Google 开发者网站政策。Java 是 Oracle 和/或其关联公司的注册商标。
最后更新时间 (UTC)：2025-02-18。
[null,null,["最后更新时间 (UTC)：2025-02-18。"],[[["\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,["# About Google Earth Engine\n\nGoogle 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------------\n\n### Geospatial 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\n### Processing 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\n### Development 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\n### Visualization 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\n### Machine 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---------------------\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."]]
