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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تولید کننده: Udemy