公告：所有在
2025 年 4 月 15 日之前注册使用 Earth Engine 的非商业项目都必须
验证是否符合非商业性质的资格条件，才能继续使用 Earth Engine。
教程
使用集合让一切井井有条
根据您的偏好保存内容并对其进行分类。
这些视频教程来自 Earth Engine 用户峰会和 Earth Outreach 数字活动中举办的讲座或实操培训。
Earth Engine 简介（精简版）
在这段节奏明快的简介中，了解开发者倡导者 Noel Gorelick 介绍的 Earth Engine。
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代码库
涵盖的主题包括分类、光谱解混和地形可视化。
表格和矢量
Earth Engine 中的表格和矢量概览。相关主题包括如何在 Earth Engine 中加载、处理、显示和分析矢量数据和表格数据。
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导入和导出
涵盖的主题包括导入和导出 Earth Engine 数据、命令行界面和地图发布。
分类
全面了解分类器（包括监督式和非监督式）、训练数据、测试数据、令人头疼的“计算值过大”消息，以及出色的线性回归精简器。
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代码库
涵盖的主题包括监督式分类和非监督式分类。
机器学习
机器学习最佳实践
在现代机器学习的快节奏下，构建和训练神经网络非常困难。了解一些最佳实践，并过滤出大量可用的信息，重点关注遥感。
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神经细分
可以说，机器学习在地球观测图像中最常见的应用是像素级分割和回归。借助神经网络，我们可以采用一系列全新的技术，这些技术在通用性方面超越了现有方法，但同时也带来了一系列挑战。了解从监督式到完全非监督式的各种应用和训练方案。
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数组和矩阵
以 Earth Engine 方式处理的数组和矩阵运算概览。主题包括线性建模、矩阵求解、特征分析、协方差缩减器。
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时间序列分析
以 Earth Engine 的方式进行时序分析的概览。涵盖的主题包括线性建模、自相关、互相关、自回归模型和平滑。
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简介 Earth Engine 与 Google Cloud Platform 之间的互操作性。
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Google 地图 API
创建“Hello World”Maps API 网页，并了解如何执行以下操作：更改选项（背景地图类型、初始位置等）、叠加数据和 KML 图层，以及开始展示您的第一张 Earth Engine 地图。
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利用 Earth Engine 结果进行发布和讲述故事
大致了解如何使用 Google 的地理位置工具来讲述您的故事并分享您的数据。主题包括 Google 地球、Google 地图 (API)、我的地图、Tour Builder 和街景。
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数据集
合成孔径雷达 (Sentinel-1)
深入了解 Earth Engine 数据目录中一个较为独特的数据集。本次会议将介绍合成孔径雷达 (SAR) 数据，以及如何使用脚本分析 Sentinel-1 SAR 数据。
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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)：2025-07-27。
[null,null,["最后更新时间 (UTC)：2025-07-27。"],[[["\u003cp\u003eThis collection provides video tutorials and resources from Earth Engine User Summits and Earth Outreach events, covering a range of topics for users of all levels.\u003c/p\u003e\n"],["\u003cp\u003eTutorials include introductory, intermediate, and advanced content on topics like machine learning, time series analysis, and working with specific datasets like Sentinel-1.\u003c/p\u003e\n"],["\u003cp\u003eEach tutorial often includes companion slides and code repositories for users to follow along and practice with.\u003c/p\u003e\n"],["\u003cp\u003eThe resources also cover integrating Earth Engine with other Google platforms, such as Google Cloud and Google Maps API, for enhanced analysis and visualization.\u003c/p\u003e\n"],["\u003cp\u003eUsers can learn how to publish and share their Earth Engine results using various tools like Google Earth, My Maps, and Tour Builder.\u003c/p\u003e\n"]]],["The video tutorials cover a range of Earth Engine topics, including loading, manipulating, and analyzing vector and tabular data. They delve into classification techniques, both supervised and unsupervised, and best practices for machine learning, including neural segmentation and remote sensing. Further, they explore array and matrix operations, time series analysis, and interoperability with Google Cloud. Tutorials also cover Google Maps API usage, sharing data through various geo tools, and working with Sentinel-1 SAR data.\n"],null,["# Tutorials\n\nThese video tutorials are from lectures or hands-on trainings conducted at Earth\nEngine User Summits and\n[Earth Outreach digital events](https://earthoutreachonair.withgoogle.com/).\n\nIntroduction to Earth Engine (condensed)\n----------------------------------------\n\nLearn about Earth Engine from developer advocate Noel Gorelick in this fast-paced intro.\n[companion slides](https://docs.google.com/presentation/d/1iZtkBNzl2HBWFT0wEhwCov89kyiBO7rSHcmMa6WNMa8) \n[code repository](https://code.earthengine.google.com/?accept_repo=users/gorelick/EE101-B) \n\nHands-on Intermediate Training\n------------------------------\n\nTopics covered include classification, spectral unmixing and terrain visualization. \n\nTables and Vectors\n------------------\n\nOverview of tables and vectors in Earth Engine. Topics include how to load, manipulate,\ndisplay and analyze vector and tabular data in Earth Engine.\n[companion slides](https://docs.google.com/presentation/d/1D7rezUHPElCfYWHMRNBChHjbEv6nXDD8xnh7_YgyK6A/edit?usp=sharing) \n\nImporting and Exporting\n-----------------------\n\nTopics covered include importing and exporting Earth Engine data, the command line interface,\nand map publishing. \n\nClassification\n--------------\n\nLearn all about classifiers (both supervised and unsupervised), training data, test\ndata, the dreaded \"computed value too large\" message, and the awesome linear\nregression reducer.\n[companion slides](https://docs.google.com/presentation/d/1esEXY4rlyl3J2oXxfhSPBHQvxie_Fmda6wSDu_S2aQo) \n[code repository](https://code.earthengine.google.com/?accept_repo=users/akarbasi/simple_classifier) \n\nTopics covered include supervised and unsupervised classification. \n\nMachine Learning\n----------------\n\n### Machine Learning Best Practices\n\nWith the pace of modern machine learning, building and\ntraining neural networks is hard. Learn some best practices and sift through the overwhelming\namount of information available with a focus on remote sensing.\n[companion slides](https://docs.google.com/presentation/d/1FCsI_X8tD3u5naij2apyzmYOBBsfd6xVWMV76y1JEqI) \n\n### Neural Segmentation\n\nArguably the most common application of ML to earth observation imagery is pixel level\nsegmentation and regression. With neural networks, a whole new set of techniques are possible\nthat eclipse existing methods in terms of generality, but come with their own set of\nchallenges. Learn applications and training regimens ranging from supervised to fully\nunsupervised.\n[companion slides](https://docs.google.com/presentation/d/1LRhtWkSLcFh0LPlWaOnHYlQUIDoCTaLIW-DY07E_wMA) \n\nArrays and Matrices\n-------------------\n\nOverview of array and matrix operations the Earth Engine way. Topics include linear\nmodeling, matrix solving, eigen analysis, covariance reducers.\n[companion slides](https://docs.google.com/presentation/d/1lPtQPK008NkQ734wCRjHAQX2xFR3a0YZuRFX3SoOMZ0) \n\nTime Series Analysis\n--------------------\n\nOverview of time series analysis the Earth Engine way. Topics covered include linear\nmodeling, auto-correlation, cross-correlation, auto-regressive models and smoothing.\n[companion slides](https://docs.google.com/presentation/d/1J1rUtf-bkfaJwYJY-tU17kzKI4U8FnF7Q2_VWqWdaak/edit?usp=sharing) \n\nEarth Engine and the Google Cloud Platform\n------------------------------------------\n\nIntroduction to interoperability between Earth Engine and the Google Cloud Platform.\n[companion slides](https://docs.google.com/presentation/d/1fEbJNe29e30s-J0vVTTLoD17nEqUUs84WNpImh6ss3U) \n\nGoogle Maps API\n---------------\n\nCreate a \"Hello World\" Maps API webpage, and learn how to do things like change the\noptions (background map type, initial location, etc.), overlay data and KML layers,\nand start to showcase your first Earth Engine maps.\n[companion slides](https://docs.google.com/presentation/d/1bVZcXIog-igAXkhmq8KUbOYY3IZ5xw6BRf3TVqOcSf4) \n\nPublishing and storytelling with your Earth Engine results\n----------------------------------------------------------\n\nGet an overview of how to use Google's geo tools to tell your stories and share your\ndata. Topics include Google Earth, Google Maps (APIs), My Maps, Tour Builder,\nand Street View.\n[companion slides](https://docs.google.com/presentation/d/13_H2ywA0MIlkhXuK-hhqB8YpO76Bqg8l0hM2xAWnvLk) \n\nDatasets\n--------\n\n### Synthetic Aperture Radar (Sentinel-1)\n\nTake a deep dive into one of the more unique datasets in the Earth Engine data catalog. This\nsession provides an introduction to Synthetic Aperture Radar (SAR) data and working with\nscripts analyzing Sentinel-1 SAR data.\n[companion slides](https://docs.google.com/presentation/d/e/2PACX-1vTT9Cw8ROlIPMgN3_k_M-zpPC97nrmuDf9dRy3q98xY-lLvi3HPlDaGbcR_pfbJNs4TBDZli9BC8WKL/pub?start=false&loop=false&delayms=3000)"]]
