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所在平台: Udemy |
课程主页: https://www.udemy.com/course/landuse-landcover-with-machine-learning-using-arcgis-only/
课程评论：没有评论
**课程名称：** 使用 ArcGIS 和机器学习进行土地利用/土地覆盖分类 **课程概述：** 本课程旨在解决用户在进行 GIS 任务时必须使用多种软件的痛点，所有操作均仅在 ArcGIS 中完成。课程涵盖从数据准备到数据可视化的所有关键环节，确保流畅高效的工作流程。重点讲解了支持向量机（SVM）和随机森林两种监督学习分类方法。 **核心内容：** * **仅限 ArcGIS 操作：** 所有土地利用/土地覆盖研究和分析都在 ArcGIS 平台内完成。 * **机器学习分类：** 深入学习使用 SVM 和随机森林等机器学习算法进行土地利用/土地覆盖分类。 * **面向专家级用户：** 假设学员已掌握 GIS 基本知识。 * **解决常见分类错误：** 详细介绍如何使用 ArcGIS 仅针对常见的像元错误分类（如河床被错误分类为城市区域）进行后处理修正。 * **土地利用变化检测：** 涵盖利用 ArcGIS 进行土地利用变化检测的方法。 * **高质量地图制作：** 教授研究级别的论文图件制作方法，满足绝大多数期刊的高质量地图要求。 * **关键技术亮点：** * 机器学习在土地利用中的应用。 * 仅使用 ArcGIS 实现土地利用/土地覆盖分类。 * 后分类像元校正技术。 * 高效的土地利用数据制作流程。 * 理解卫星图像的红外波段。 * 土地利用变化检测。 * 混淆矩阵的制作与变化计算。 * 特定区域（如城市、裸地、河床）的误分类纠正。 * 高精度土地覆盖制图。 * ArcGIS 中的机器学习技术。 * 土地利用制图技术。 * 土地覆盖分类误差的修正。
This on-demand course was created in response to user requests. Many users expressed frustration with having to use multiple software programs for GIS tasks, such as performing land use classification in one program, land use change detection in another, and pixel correction (post-classification) in yet another. In this course, all tasks are performed exclusively using ArcGIS. From data preparation to data representation, this course covers every important task, ensuring a seamless and efficient workflow within ArcGIS. This course covers SVM and random forest methods for classification with supervised methods. So all the landuse is not perfect some pixels remain wrong classified such as sometimes the river bed is classified as an urban area. This is a common problem in most landuse classifications. So in this course, I have covered how to correct this type of error pixels using ArcGIS only. Landuse change using ArcGIS is also covered. Research-level layout creation is also covered and accepted by most journals with high-quality maps. Key Highlights:Landuse using machine learningUsing only and only ArcGISPost classification pixel correctionFast method of landuse makingUnderstanding of satellite image in infrared. Landuse change detection.Making of confusion matrix and calculation of changes. Note: This is an expert-level course so I assume you know all the basics of GIS. Highlights:Land use mappingLand cover classificationArcGIS machine learningSVM land use classificationRandom Forest land use mappingPost-classification pixel correctionArcGIS pixel correctionSupervised training ArcGISLand use errors correctionUrban area misclassification correctionsBarren land classification corrections Riverbed misclassification correctionsSingle software land use mappingArcGIS only land use mappingHigh-accuracy land cover mappingMachine learning in ArcGISLand use mapping techniquesLand cover classification errors corrections
