Machine Learning in GIS : Understand the Theory and Practice
What you'll learn
- Fully understand the basics of Machine Learning
- Get an introduction to Geographic Information Systems (GIS), geodata types and GIS applications
- Fully understand basics of Remote Sensing
- Learn open source GIS and Remote Sensing software tools (QGIS, Google Earth Engine and others)
- Fully understand the main types of Machine Learning and their applications in GIS
- Learn about supervise and unsupervise learning and their applications in GIS
- Learn how to apply supervised and unsupervised Machine Learning algorithms in QGIS and Google Earth Engine
- Understand what is segmentation, object-based image analysis (OBIA) and predictive modeling in GIS
- Learn how to perform image segmentation with Orfeo Toolbox
- Understand the main developments in the field of Artificial Intelligence, deep learning and machine learning as applied to GIS
- A working computer
This course is designed to equip you with the theoretical and practical knowledge of Machine Learning as applied for geospatial analysis, namely Geographic Information Systems (GIS) and Remote Sensing. By the end of the course, you will feel confident and completely understand the Machine Learning applications in GIS technology and how to use Machine Learning algorithms for various geospatial tasks, such as land use and land cover mapping (classifications) and object-based image analysis (segmentation). This course will also prepare you for using GIS with open source and free software tools.
In the course, you will be able to apply such Machine Learning algorithms as Random Forest, Support Vector Machines and Decision Trees (and others) for classification of satellite imagery. On top of that, you will practice GIS by completing an entire GIS project by exploring the power of Machine Learning, cloud computing and Big Data analysis using Google Erath Engine for any geographic area in the world.
The course is ideal for professionals such as geographers, programmers, social scientists, geologists, and all other experts who need to use maps in their field and would like to learn more about Machine Learning in GIS. If you're planning to undertake a task that requires to use a state of the art Machine Learning algorithms for creating, for instance, land cover and land use maps, this course will give you the confidence you need to understand and solve such geospatial problem.
One important part of the course is the practical exercises. You will be given some precise instructions and datasets to create maps based on Machine Learning algorithms using the QGIS software and Google Earth Engine.
In this course, I include downloadable practical materials that will teach you:
- How to install open source GIS (QGIS, OTB toolbox) software on your computer and correctly configure it
- QGIS software interface including its main components and plug-ins
- Learn how to classify satellite images with different machine learning algorithms (random forest, support vector machines, decision trees and so on) in QGIS
- Learn how to perform image segmentation in QGIS
- Learn how to prepare your first land cover map using the cloud computing Google Earth Engine Platform.
Who this course is for:
- Geographers, programmers, geologists, biologists, social scientists, or every other expert who deals with GIS maps in their field
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 #Beginners
2. Remote Sensing in QGIS: Fundamentals of Image Analysis 2020
3. Core GIS: Land Use and Land Cover & Change Detection in QGIS
4. Machine Learning in GIS: Understand the Theory and Practice
5. Machine Learning in GIS: Land Use/Land Cover Image Analysis
6. Machine Learning in ArcGIS: Map Land Use/ Land Cover in GIS
7. Object-based image analysis & classification in QGIS/ArcGIS
8. ArcGIS: Learn Deep Learning in ArcGIS to advance GIS skills
8. Google Earth Engine for Big GeoData Analysis: 3 Courses in 1
10. Google Earth Engine for Machine Learning & Change Detection
11. QGIS & Google Earth Engine for Environmental Applications
12. Advanced Remote Sensing Analysis in QGIS and on cloud
Option 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 1
2. Machine Learning in GIS and Remote Sensing: 5 Courses in 1
3. Google Earth Engine for Big GeoData Analysis: 3 Courses in 1
4. Google Earth Engine for Machine Learning & Change Detection
5. Advanced Remote Sensing Analysis in QGIS and on cloud