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Updated in [August 21st, 2023]

What skills and knowledge will you acquire during this course?
By the end of this course, the learner will acquire the following skills and knowledge:

1. Theoretical understanding of Machine Learning as applied to Geographic Information Systems (GIS) and Remote Sensing.
2. Practical knowledge of using Machine Learning algorithms for geospatial tasks such as land use and land cover mapping and object-based image analysis.
3. Proficiency in using Machine Learning algorithms like Random Forest, Support Vector Machines, and Decision Trees for satellite image classification.
4. Ability to apply GIS techniques using open source software tools like QGIS and Google Earth Engine.
5. Understanding of cloud computing and Big Data analysis in the context of GIS.
6. Competence in installing and configuring open source GIS software on a computer.
7. Familiarity with the QGIS software interface and its main components and plug-ins.
8. Hands-on experience in performing image segmentation and creating land cover maps using QGIS and Google Earth Engine.
9. Confidence in using state-of-the-art Machine Learning algorithms for geospatial problem-solving.
10. Relevance for professionals in various fields such as geographers, programmers, social scientists, and geologists who need to use maps in their work.

How does this course contribute to professional growth?
This course on Machine Learning in GIS contributes to professional growth by providing professionals with the theoretical and practical knowledge of applying Machine Learning algorithms for geospatial analysis. By completing this course, professionals will gain confidence in using Machine Learning applications in GIS technology and will be able to utilize Machine Learning algorithms for various geospatial tasks such as land use and land cover mapping and object-based image analysis. Additionally, professionals will be prepared to use GIS with open source and free software tools. This course is ideal for geographers, programmers, social scientists, geologists, and other experts who need to use maps in their field and want to learn more about Machine Learning in GIS. The practical exercises included in the course allow professionals to apply their knowledge and skills using QGIS software and Google Earth Engine. Overall, this course equips professionals with the necessary skills and knowledge to confidently solve geospatial problems using state-of-the-art Machine Learning algorithms.

Is this course suitable for preparing further education?
Yes, this course is suitable for preparing further education.

Course Syllabus

Introduction to the course, GIS and Remote Sensing

Installation of QGIS on your Computer

Introduction to Machine Learning in GIS

Types of supervised & unsupervised machine learning and applications in GIS

New: Image classification in QGIS: how to create training and run classification

Machine Learning in Google Earth Engine

Introduction to object-based machine learning in GIS and QGIS

Predictions and regression in GIS and deep learning for Big Data Analysis

Final Project: Machine Learning for GIS on cloud (Google Earth Engine)