Landuse landcover with machine learning using ArcGIS only

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 la