Machine Learning in R: Land Use Land Cover Image Analysis

Learn supervised machine learning for Remote Sensing R & R-Studio, image classification, land use and land cover mapping

Created by: Kate Alison
GIS & Data Science

What Will I Learn?

• Learn supervised machine learning for image classification using R-programming language in R-Studio
• Learn theoretical background of Machine Learning
• Apply machine learning based algorithms (random forest, SVM) for image classification analysis in R and R-Studio
• Learn R-programming from scratch: R crash course is included that you could start R-programming for machine learning
• Fully understand the basics of Land use and Land Cover (LULC) Mapping based on satellite image classification
• Get an introduction and fully understand to Remote Sensing relevant for LULC mapping
• Pre-process and analyze Remote Sensing images in R
• Learn how to create training and validation data for image classification in QGIS
• Build machine learning based image classification models for LUCL analysis and test their robustness in R
• Implement Machine Learning algorithms, such as Random Forests, SVM in R
• Apply accuracy assessment for Machine Learning based image classification in R
• You'll have a copy of the scripts and step-by-step manuals used in the course for your reference to use in your analysis.

Requirements

• Availability computer and internet & strong interest in the topic
• The course will be demonstrated on Windows PC. Mac and Linux users will have to adapt the instructions to their operating systems.

Target audience

• Everyone who would like to learn Data Science Applications in the R & R Studio Environment
• Geographers, Programmers, geologists, biologists, social scientists, or every other expert who deals with GIS maps in their field