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Machine Learning in R: Land Use Land Cover Image Analysis

سرفصل های دوره

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


1. Introduction
  • 1. Introduction
  • 2. What is R and RStudio
  • 3. How to install R and RStudio in 2021
  • 4. Lab Install R and RStudio in 2021
  • 5. Lab Installing QGIS and install SCP
  • 6. A note on QGIS versions and its plug-ins

  • 2. Machine Learning for image classification theory overview
  • 1. Introduction to Machine Learning
  • 2. Basics of machine learning for classification analysis
  • 3. Common algorithms of image classification

  • 3. Introduction to R-Studio and R crash course
  • 1. Lab Introduction to RStudio Interface
  • 2. Lab Installing Packages and Package Management in R
  • 3. Variables in R and assigning Variables in R
  • 4. Lab Variables in R and assigning Variables in R
  • 5. Overview of data types and data structures in R
  • 6. Lab data types and data structures in R
  • 7. Vectors operations in R
  • 8. Data types and data structures Factors
  • 9. Dataframes overview in R
  • 10. Functions in R - overview
  • 11. For Loops in R
  • 12. Read Data into R
  • Files.zip

  • 4. Basics of Remote Sensing for LULC mapping theory overview
  • 1. Introduction to digital image
  • 2. Sensors and Platforms
  • 3. Understanding Remote Sensing for LULC mapping
  • 4. Stages of LULC supervised classification

  • 5. Satellite image preparation in R for Land use land cover (LULC) analysis in R
  • 1. Data used for analysis Landsat images
  • 2. Preprocessing of satellite image data
  • 3. Overview of processing steps in R for Landsat images
  • 4. Lab Image load in R
  • 5. Lab Image Layerstacks in R
  • 6. Lab Batch Processing in R unzipp, laerstack of LAndsat images
  • 7. Visualize images in R
  • Files.zip

  • 6. Training data Preparation in R for Machine Learning image classification
  • 1. Data used for analysis Sentinel images
  • 2. Training data requirements for classification and training data selection
  • 3. Lab Prepare training data in R - part 1
  • 4. Lab Prepare training data in R - part 2
  • 5. Plotting spectral signatures in R
  • Files.zip

  • 7. Land UseLand Cover Image Classification using Machine Learning algorithms in R
  • 1. Image Classification in R with Random Forest in R
  • 2. Map visualization Creating classified image based on Random Forest model in R
  • 3. Map visualization Create a classified image based on RF model in QGIS
  • 4. Image Classification in R with Support Vector Machines (SVM) in R
  • 5. Accuracy assessment of image classification
  • 6. Lab Accuracy Assessment (validation) of classification in R
  • 7. Independent Task Accuracy assessment for SVM-based classification
  • 8. Lab Creating a LULC map of your final image classification result in QGIS
  • 9. BONUS
  • Files.zip
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    تاریخ انتشار: 15 آبان 1403
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