Machine Learning in R: Land Use Land Cover Image Analysis

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Kate Alison

5:38:27

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1. Introduction

1. Introduction.mp4

06:42

2. What is R and RStudio.mp4

02:43

3. How to install R and RStudio in 2021.mp4

03:40

4. Lab Install R and RStudio in 2021.mp4

05:33

5. Lab Installing QGIS and install SCP.mp4

12:39

6. A note on QGIS versions and its plug-ins.mp4

08:44

2. Machine Learning for image classification theory overview

1. Introduction to Machine Learning.mp4

16:03

2. Basics of machine learning for classification analysis.mp4

09:27

3. Common algorithms of image classification.mp4

19:18

3. Introduction to R-Studio and R crash course

1. Lab Introduction to RStudio Interface.mp4

09:37

2. Lab Installing Packages and Package Management in R.mp4

04:19

3. Variables in R and assigning Variables in R.mp4

02:19

4. Lab Variables in R and assigning Variables in R.mp4

01:45

5. Overview of data types and data structures in R.mp4

08:19

6. Lab data types and data structures in R.mp4

09:57

7. Vectors operations in R.mp4

07:37

8. Data types and data structures Factors.mp4

02:32

9. Dataframes overview in R.mp4

03:21

10. Functions in R - overview.mp4

04:50

11. For Loops in R.mp4

03:56

12. Read Data into R.mp4

05:13

Files.zip

4. Basics of Remote Sensing for LULC mapping theory overview

1. Introduction to digital image.mp4

11:21

2. Sensors and Platforms.mp4

05:01

3. Understanding Remote Sensing for LULC mapping.mp4

06:59

4. Stages of LULC supervised classification.mp4

12:18

5. Satellite image preparation in R for Land use land cover (LULC) analysis in R

1. Data used for analysis Landsat images.mp4

05:06

2. Preprocessing of satellite image data.mp4

05:01

3. Overview of processing steps in R for Landsat images.mp4

02:37

4. Lab Image load in R.mp4

07:15

5. Lab Image Layerstacks in R.mp4

08:59

6. Lab Batch Processing in R unzipp, laerstack of LAndsat images.mp4

05:17

7. Visualize images in R.mp4

07:43

Files.zip

6. Training data Preparation in R for Machine Learning image classification

1. Data used for analysis Sentinel images.mp4

06:18

2. Training data requirements for classification and training data selection.mp4

07:15

3. Lab Prepare training data in R - part 1.mp4

07:16

4. Lab Prepare training data in R - part 2.mp4

09:40

5. Plotting spectral signatures in R.mp4

05:28

Files.zip

7. Land UseLand Cover Image Classification using Machine Learning algorithms in R

1. Image Classification in R with Random Forest in R.mp4

14:29

2. Map visualization Creating classified image based on Random Forest model in R.mp4

09:15

3. Map visualization Create a classified image based on RF model in QGIS.mp4

05:31

4. Image Classification in R with Support Vector Machines (SVM) in R.mp4

15:48

5. Accuracy assessment of image classification.mp4

11:13

6. Lab Accuracy Assessment (validation) of classification in R.mp4

11:33

7. Independent Task Accuracy assessment for SVM-based classification.mp4

01:15

8. Lab Creating a LULC map of your final image classification result in QGIS.mp4

05:03

9. BONUS.mp4

02:12

Files.zip

Description

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

What You'll 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.

Who is this for?

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

What You Need to Know?

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.

More details

Description
Mastering Machine Learning in R and R-Studio: Image Classification for Land Use and Land Cover (LULC) Mapping
Welcome to this unique Udemy course on Machine Learning in R and R-Studio, focusing on image classification for land use and land cover (LULC) mapping!
Why Should Geospatial Analysts (GIS, Remote Sensing) Learn R?
This course is a pioneering offering on Udemy, providing you with the opportunity to acquire highly sought-after R programming skills for Remote Sensing-based Machine Learning analysis in R.
The knowledge you gain in this course will empower you to embark on your own Machine Learning image data analysis in R. With over 2 million R users worldwide, Oracle has solidified R's position as a leading programming language in statistics and data science. The R user base grows by approximately 40% each year, and an increasing number of organizations rely on it for their day-to-day operations. By enrolling in this course today, you are taking a proactive step to future-proof your career!
Course Highlights:
This comprehensive course comprises 7 sections, meticulously covering every aspect of Machine Learning, encompassing both theory and practice. You will:
Gain a solid theoretical foundation in Machine Learning.
Master supervised machine learning techniques for image classification.
Apply machine learning algorithms (such as random forest and SVM) for image classification analysis in R and R-Studio.
Acquire a fundamental understanding of R programming.
Fully grasp the basics of Land Use and Land Cover (LULC) Mapping based on satellite image classification.
Comprehend the fundamentals of Remote Sensing pertinent to LULC mapping.
Learn how to create training and validation datasets for image classification in QGIS.
Build machine learning-based image classification models for LULC analysis and evaluate their robustness in R.
Apply accuracy assessment to Machine Learning-based image classification in R.
No Prior R or Statistics/Machine Learning/R Knowledge Required:
This course begins with a comprehensive introduction to the most essential Machine Learning concepts and techniques. I employ easy-to-follow, hands-on methods to demystify even the most intricate R programming concepts, especially in the context of satellite image analysis.
Throughout the course, you will implement these techniques using real image data sourced from various providers, including Landsat and Sentinel images. As a result, upon completion of this Machine Learning course in R for image classification and LULC analysis, you will possess the skills to work with diverse data streams and data science packages to analyze real data in R.
If this is your initial encounter with R, rest assured. This course serves as a comprehensive introduction to R and R programming.
What Sets This Course Apart?
This course distinguishes itself from other training resources by delivering practical, hands-on solutions in an easy-to-follow manner, aimed at enhancing your GIS and Remote Sensing skills, as well as your proficiency in R. You will be equipped to initiate spatial data analysis for your own projects, earning recognition from future employers for your advanced GIS capabilities, mastery of cutting-edge machine learning algorithms, and R programming proficiency.
Integral to the course are practical exercises. You will receive precise instructions, scripts, and datasets to execute Machine Learning algorithms using R tools.
Join This Course Now and Elevate Your Expertise!
Who this course is for:
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

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Machine Learning

Kate Alison

Instructor's Courses

I am a passionate data science expert and educator. I do regular teaching and training all over the world. I have many satisfied students! And now I will be glad if I can teach also you these interesting, highly applied, and exciting topics!For GIS & Remote Sensing students:Order of how to take my courses:Option 1: Take all individual courses that contain more details and more labs in the following order:1. Get started with GIS & Remote Sensing in QGIS #Beginners2. Remote Sensing in QGIS: Fundamentals of Image Analysis 20203. Core GIS: Land Use and Land Cover & Change Detection in QGIS4. Machine Learning in GIS: Understand the Theory and Practice5. Machine Learning in GIS: Land Use/Land Cover Image Analysis6. Machine Learning in ArcGIS: Map Land Use/ Land Cover in GIS7. Object-based image analysis & classification in QGIS/ArcGIS8. ArcGIS: Learn Deep Learning in ArcGIS to advance GIS skills8. Google Earth Engine for Big GeoData Analysis: 3 Courses in 110. Google Earth Engine for Machine Learning & Change Detection11. QGIS & Google Earth Engine for Environmental Applications12. Advanced Remote Sensing Analysis in QGIS and on cloudOption 2: Take my combi-courses that contain summarized information from the above courses, though in fewer details (labs, videos):1. Geospatial Data Analyses & Remote Sensing: 4 Classes in 12. Machine Learning in GIS and Remote Sensing: 5 Courses in 13. Google Earth Engine for Big GeoData Analysis: 3 Courses in 14. Google Earth Engine for Machine Learning & Change Detection5. Advanced Remote Sensing Analysis in QGIS and on cloud

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