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所在平台: Udemy |
课程主页: https://www.udemy.com/course/landcover-classification-using-google-earth-engine-gee/
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课程名称：使用 Google Earth Engine (GEE) 进行地表覆盖分类 课程概述：近年来，各种空基和地基传感器获取的遥感（RS）数据集数量显著增加，这些数据集在光谱、空间、时间和辐射分辨率等特性上各不相同。处理大量的RS数据集是一项具有挑战性的任务，且有其特殊需求。云计算平台是存储、访问和分析大数据集的有效方式，能够在非常强大的服务器上虚拟化超级计算机。Google Earth Engine（GEE）是一个基于云的地理空间处理平台，由Google于2010年推出。GEE利用Google的计算基础设施和可用的开放访问遥感数据集，是最受欢迎的大型地理数据处理平台，为用户提供对众多遥感数据集的免费访问，促进科学发现。用户可以通过Python和JavaScript应用程序接口（API）访问GEE，而JavaScript API则可以通过一个名为代码编辑器的web集成开发环境（IDE）访问。在该平台上，用户可以编写和执行脚本，以分享和重复地理空间分析及处理工作流。GEE的卓越能力为地球科学的各个学科提供了广泛的应用机会。地表覆盖信息在科学、经济和政治等多个方面都起着至关重要的作用。准确的地表覆盖信息直接影响后续应用的准确性，因此对及时和准确的地表覆盖信息的需求很高。在过去十年的地表覆盖分类研究中，使用时间序列卫星图像通常能够产生比单日图像更高的准确性。最近，Google Earth Engine（GEE）的可用性吸引了对基于遥感的应用程序的关注，其中时间序列图像衍生的时间聚合方法（例如使用均值或中位数等指标）被广泛应用，而不是直接使用时间序列图像。在GEE中，许多研究简单地选择尽可能多的图像来填补空缺，而没有考虑不同年份/季节的图像可能如何影响分类准确性。
In Recent years, there has been a significant increase in the number of remote sensing (RS) datasets acquired by various spaceborne and airborne sensors with different characteristics (e.g., spectral, spatial, temporal, and radiometric resolutions). Working with petabytes of RS datasets is a challenging task and has its own special requirements. Cloud computing platforms are efficient ways of storing, accessing, and analyzing datasets on very powerful servers, which virtualize supercomputers for the user. These systems provide infrastructure, platform, storage services, and software packages in a variety of ways for the customers. Google Earth Engine (GEE) is a cloud-based geospatial processing platform which was launched by Google, in 2010. GEE uses Google's computational infrastructure and available open-access RS datasets. GEE is the most popular big geo data processing platform, facilitating the scientific discovery process by providing users with free access to numerous remotely sensed datasets. Earth Engine is available through Python and JavaScript Application Program Interfaces (APIs). The JavaScript API is accessible via a web-based Integrated Development Environment (IDE) called the Code Editor. This platform is where users can write and execute scripts to share and repeat geospatial analysis and processing workflows. The remarkable capabilities of GEE provide opportunities to employ this platform in broad variety of disciplines in all branches of Earth science studies. Land cover information plays a vital role in many aspects of life, from scientific and economic to political. Accurate information about land cover affects the accuracy of all subsequent applications, therefore accurate and timely land cover information is in high demand. In land cover classification studies over the past decade, higher accuracies were produced when using time series satellite images than when using single date images. Recently, the availability of the Google Earth Engine (GEE), a cloud-based computing platform, has gained the attention of remote sensing based applications where temporal aggregation methods derived from time series images are widely applied (i.e., the use the metrics such as mean or median), instead of time series images. In GEE, many studies simply select as many images as possible to fill gaps without concerning how different year/season images might affect the classification accuracy.
