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所在平台: Udemy |
课程主页: https://www.udemy.com/course/predict-just-about-anything-with-google-earth-engine/
课程评论：没有评论
**课程名称：** 使用Google Earth Engine预测一切 (第一部分) **课程概述：** 本课程专注于数据准备和采样，这是构建预测模型，尤其是在精准农业领域中至关重要但常被忽视的一环。通过Google Earth Engine平台，本课程将教授一套实用的工具和工作流程，帮助您识别、提取具有预测能力的协变量，满足您在研究中提出的复杂问题。尽管课程偏向草本作物，但所介绍的工具具有高度灵活性，可适应各种研究需求。 **学习内容：** * **数据处理与过滤：** * 解决云层和阴影问题的实用方案。 * 对影像集合进行过滤。 * 将函数映射到影像集合。 * 对影像和影像集合应用掩膜。 * 合成RGB影像。 * **分类与采样：** * 土地覆盖/土地利用 (LCLU) 分类。 * 分层和均衡采样策略。 * 划分训练集和验证集。 * **结果可视化与评估：** * 结果可视化。 * 添加图例。 * 分类准确性评估。 * 计算每个LCLU类别的面积（公顷）。 * **数据导出与重用：** * 将LCLU栅格数据导出为Asset以供重用。 * 将栅格数据转换为矢量数据并导出为Asset以供重用。 * **时间序列构建与分析：** * 构建时空聚合时间序列。 * 将Sentinel-1 SAR数据从线性转换为分贝。 * 从Sentinel-1计算土壤湿度。 * 通过普通最小二乘法回归从重力测量结果推断NDVI。（注意：此处原文为“Infer NDVI from Gravimetry through Ordinary Least Squares regression”，可能存在小偏差。） * 使用均衡样本计算长期统计数据。 * 识别地块中表现最佳/最差的像素以进行纠正。 * **协变量筛选与复杂时间序列：** * 筛选用于预测目的的协变量。 * 构建日益复杂和精细的时间序列的工具。 * 聚合不同数据集在不同时间粒度的数据。 * 创建地块/区域/像素以进行有意义的聚合。 * **数据导出：** * 将数据导出为CSV格式。 **目标受众：** 希望掌握如何准备和处理地理空间数据，以进行预测模型开发和研究的人员，尤其是在精准农业等领域。
Would you like to be able to develop and prepare the data you need to pose, explore, and answer the most pressing and complex questions in your field of research? This course concerns itself with one of the most demanding and least covered parts of developing a predictive model for precision agriculture, or just about anything: sampling. When studying machine learning through video tutorials you normally access somebody's dataset and learn how to apply algorithms. But how were those neat datasets created? This course details how to use and adapt to your unique needs some tools I developed to sample just about any spatially explicit variable through the Google Earth Engine Platform. This course is biased in favor of herbaceous crops but the tools presented are flexible enough to be adapted to your research interests. In this course you will learn about a complete workflow to identify and extract covariates with predictive power:· One practical solution to cloud and shadows filtering.· Filtering a collection.· Mapping a function over a collection.· Applying masks to images and image collections.· Composite RGB images.· Land Cover Land Use (LCLU) Classification.· Stratified and balanced sampling strategies.· Split training and validation dataset.· Results visualization.· Add a legend.· Classification accuracy assessment.· Computing area in hectares for each LCLU class.· Exporting LCLU raster data as assets for reuse.· Convert raster to vector data and export as assets for reuse.· Build time series aggregating spatially and over time windows.· Convert Sentinel 1 SAR data from linear to decibel.· Compute soil moisture from Sentinel 1.· Infer NDVI from Gravimetry through Ordinary Least Squares regression.· Compute long term statistics using balanced samples.· Identify the lowest/highest performing pixels on the property for corrective purposes.· Screen covariates for predictive purposes.· Tools to build increasingly elaborate and complex time series.· Aggregate data from different datasets at different time granules.· Create parcels/paddocks/pixels for meaningful aggregation.· Export the data as.CSV
