Geospatial Data Analyses & Remote Sensing: 5 Classes in 1

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Kate Alison

8:47:07

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1 - Introduction to the course GIS and Remote Sensing

1 - Introduction to the course.mp4

04:22

2 - Applications of GIS.mp4

06:51

3 - Definition of GIS.mp4

01:26

4 - Applications of Remote Sensing.mp4

07:41

5 - Definition of Remote Sensing.mp4

05:12

2 - Software used in this course

6 - About opensource QGIS software.mp4

05:47

6 - Software-to-use-for-the-course-2019.pdf

7 - QGIS version information.mp4

02:21

8 - Lab QGIS installation.mp4

12:39

8 - Practical-1-2.zip

8 - Practical-1-Interface-of-QGIS.pdf

9 - SemiAutomatic Classification Plugin for QGIS.mp4

02:56

10 - Lab QGIS interface.mp4

07:45

11 - Lab QGIS Toolbars.mp4

09:46

12 - OTB installation.mp4

02:12

12 - OTB-Installation-Guide-udemy-240320.pdf

13 - Lab Creating account in Google Earth Engine.mp4

03:37

3 - Basics of GIS

14 - Main principles of GIS.mp4

03:57

15 - Basics of Geodata and its main types.mp4

02:43

16 - GIS software and your PC set up.mp4

11:59

17 - Lab Your First GIS Map in QGIS.mp4

12:35

17 - Materials.zip

4 - Introduction to Remote Sensing theory

18 - Sensors and Platforms.mp4

05:01

19 - Introduction to digital images.mp4

11:21

20 - Download-images-SCP.pdf

20 - Lab How to download satellite images with SCP plugin.mp4

10:20

20 - Practical2-3-sentinel2-sample-1.zip

5 - Practicals basics of satellite image analysis Remote Sensing in QGIS

21 - Lab Layerstacking True and False Colour composites.mp4

15:15

21 - Landsat5-Image.zip

22 - Lab Image preprocessing atmospheric correction.mp4

07:50

22 - Landsat-8-orig.7z

23 - Sources of Remote Sensing images.mp4

11:20

24 - EO browser Using cloud platform for spectral indices & land cover analysis.mp4

19:15

6 - Basics of land use and land cover LULC mapping theory

25 - Introduction Machine Learning.mp4

09:27

26 - Understanding Remote Sensing for LULC mapping.mp4

06:59

27 - Introduction to LULC classification based on satellite images.mp4

03:09

28 - Supervised and unsupervised image classification.mp4

09:31

29 - Unsupervised Kmeans image analysis in QGIS.mp4

04:52

29 - bonn-2019-july-s2.zip

30 - Stages of LULC supervised classification.mp4

12:18

31 - Overview of image classification algorrithms.mp4

19:18

32 - Accuracy assessment of LULC map.mp4

11:13

7 - Practicals Image CLassification in SemiAutomated Classification Plugin in QGIS

33 - LT52300691985196CUB00-MTL.txt

33 - Lab Training data collection in QGIS.mp4

24:06

33 - Landsat5-Amazon-Rainforest.7z

33 - Landsat5-Image.zip

33 - Training-Landsat5.zip

34 - Lab LULC with the use of spectral angle mapping.mp4

09:17

35 - Lab LULC with the use of Maximum Likelihood Algorithm.mp4

05:28

36 - Lab LULC with the use of Minimum Distance Classification Algorithm.mp4

03:51

37 - Lab Validation data creation.mp4

12:44

37 - Validation-Landsat5.zip

38 - Lab Accuracy Assessment.mp4

13:37

39 - Landsat8-class.zip

39 - Landsat-8-orig.zip

39 - Practical-LULC-Landsat.pdf

39 - Project LULC mapping of Landsat 8.mp4

01:46

8 - Introduction to change detection in QGIS

40 - Introduction to change detection.mp4

07:35

41 - Change-detection.pdf

41 - Change-detection.zip

41 - Lab Change Detection in QGIS.mp4

14:25

42 - Lab How to make a map in QGIS.mp4

07:23

9 - Image Classification in Google Earth Engine

43 - Section Overview.html

44 - Supervised classification with Google Earth Engine explorer.mp4

18:57

45 - Import images and their visualization in Google Earth Engine.mp4

16:11

45 - Lab1-GEE-import-data.pdf

46 - Lab2-GEE-kmeans.pdf

46 - Unsupervised Kmeans image analysis in Google Earth Engine.mp4

08:34

47 - Random Forest Supervised CLassification in Earth Engine.mp4

17:34

47 - Supervised-classification-RF.txt

48 - Accuracy Assessment in Earth Engine.mp4

05:53

48 - Accuracy-Supervised-classification-RF.txt

10 - Introduction to Machine Learning in GIS

49 - Introduction to Machine Learning.mp4

16:03

50 - On Machine Learning in GIS and Remote Sensing.mp4

08:19

51 - Supervised and Unsupervised Learning classification in GIS and Remote Sensing.mp4

09:27

52 - Object detection in GIS.mp4

05:31

53 - Segmentation and objectbased image analysis OBIA.mp4

04:42

54 - Prediction in GIS and deep learning for Big Data Analysis.mp4

07:08

55 - Project Machine Learning for GIS on cloud Google Earth Engine.mp4

06:14

11 - Machine Learning and Objectbased Analysis OBIA in GIS part 2

56 - Section Overview.html

57 - Objectbased image classification OBIA VS pixelbased image classification.mp4

06:09

58 - OBIA-S2.zip

58 - Object-Based-Classification.pdf

58 - Segmentation of highresolution satellite image.mp4

07:36

59 - Creating training data from satellite image based on the segmented layer.mp4

08:09

60 - Objectbased image classification with the Machine Learning algorithm.mp4

11:00

61 - Final Project Instructions.mp4

06:17

12 - BONUS

62 - BONUS.mp4

02:13

62 - Resources-17062020.pdf

Description

Learn Remote Sensing, QGIS & GIS , main concepts, machine learning, QGIS classification, change detection, Earth Engine

What You'll Learn?

Understand and implement basic concepts of Geographic Information Systems (GIS) and Remote Sensing
Fully understand the basics of Land use and Land Cover (LULC) Mapping and Change Detection in QGIS
Learn the most popular open-source GIS and Remote Sensing software tools (QGIS), Semi-automated classification (SCP) plugin, OTB toolbox)
Learn how to obtain satellite data, apply image preprocessing, create training and validation data in QGIS
Create your first GIS maps for your reports/presentations in QGIS
Understand machine learning concepts and its application in GIS and Remote Sensing
Apply Machine Learning image classification mapping and change detection in SCP, OTB toolbox and QGIS
Fully understand and apply advanced methods in machine learning in GIS and Remote Sensing, such as random forest classification and object-based image analysis,
You'll have a copy of the labs, step-by-step manuals and scripts used in the course for QGIS & more

Who is this for?

Geographers, Programmers, geologists, biologists, social scientists, or everyone who deals with GIS maps in their field or would like to learn GIS and Remote Sensing

What You Need to Know?

A working computer

An interest in working with spatial data

The course will be demonstrated using a QGIS version of Windows PC. Mac and Linux users will have to adapt the instructions to their operating systems.

More details

Description
Geospatial Analyses & Remote Sensing : from Beginner to Pro
Are you struggling to create GIS or satellite imagery-based maps for your Remote Sensing or GIS project? Do terms like Remote Sensing object-based image analysis, machine learning, QGIS, or Google Earth Engine sound daunting? Are you seeking a practical course that guides you through the concepts and helps you embark on real-life GIS mapping projects?
Welcome to our Practical Geospatial Masterclass, combining the content of four separate courses into one comprehensive learning experience. With over nine hours of video content, hands-on exercises, and downloadable materials, this course equips you with the knowledge and skills required for practical geospatial analysis. You'll learn to perform tasks such as land use and land cover mapping, change detection, machine learning for GIS, data manipulation, and map creation, all using popular and FREE software tools.
Course Highlights:
Comprehensive theoretical and practical geospatial knowledge
Application of Machine Learning in GIS and Remote Sensing
Land use and land cover mapping
Object-based image analysis
Data processing and map creation
Practical exercises with QGIS and Google Earth Engine
Course Focus:
This masterclass is designed to empower you with both theoretical and practical geospatial analysis skills, covering Remote Sensing, Geographic Information Systems (GIS), and Machine Learning applications in GIS and Remote Sensing technology. By course completion, you'll have a strong understanding of Remote Sensing and GIS fundamentals, Machine Learning applications in geospatial tasks, and the use of Machine Learning algorithms for land use and land cover mapping and object-based image analysis. Additionally, you'll be well-prepared to perform geospatial and Remote Sensing analysis using open source and free software tools.
What You'll Learn:
Practical use of Machine Learning algorithms in QGIS
Downloading and processing satellite imagery
Supervised and unsupervised learning
Accuracy assessment and change detection
Object-based image analysis
Cloud computing and Big Data analysis using Google Earth Engine
Who Should Enroll:
This course is ideal for professionals including geographers, programmers, social scientists, geologists, GIS & Remote Sensing experts, and anyone seeking to enhance their GIS and Remote Sensing skills. Whether you're a novice or looking to advance your knowledge in Machine Learning for GIS and Remote Sensing, this course provides the confidence and skills needed to tackle geospatial challenges.
INCLUDED IN THE COURSE: Gain access to precise instructions, downloadable practical materials, scripts, and datasets for hands-on geospatial analysis using QGIS and Google Earth Engine. Enroll today to unlock the power of practical geospatial analysis!
Who this course is for:
Geographers, Programmers, geologists, biologists, social scientists, or everyone who deals with GIS maps in their field or would like to learn GIS and Remote Sensing

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GIS

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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