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
Are you ready to apply Machine Learning to real geospatial problems but unsure how to begin? This course provides a clear, practical introduction to Machine Learning for Geographic Information Systems (GIS) and Remote Sensing. You will learn both the theoretical foundations and the hands-on workflows needed to use Machine Learning for land use and land cover mapping, image classification, segmentation, and other essential geospatial tasks.
Designed for learners who want practical skills rather than abstract theory, this course demonstrates how to run Machine Learning algorithms in QGIS, how to perform segmentation and object-based analysis, and how to use Google Earth Engine for cloud-based mapping with satellite images.
Course Highlights
• Theoretical and practical knowledge of Machine Learning in GIS and Remote Sensing
• Hands-on application of Machine Learning algorithms such as Random Forest, SVM, and Decision Trees
• Execution of a complete GIS project with real data
• Cloud-based geospatial processing using Google Earth Engine
• Clear, step-by-step demonstrations with downloadable materials
• Practical examples suitable for academic, research, and professional use
Course Focus
This course integrates Machine Learning theory with real geospatial workflows. You will learn how to preprocess satellite images, classify them using modern algorithms, run segmentation workflows, and create land cover maps in QGIS and Google Earth Engine. By the end of the course, you will have the skills and confidence to apply Machine Learning to a wide range of Remote Sensing and GIS applications.
What You Will Learn
• Installing and configuring open-source GIS tools such as QGIS and the OTB toolbox
• Navigating the QGIS interface, plug-ins, and processing tools
• Classifying satellite images using Random Forest, SVM, Decision Trees, and other algorithms
• Performing image segmentation and object-based analysis in QGIS
• Creating land cover maps using Google Earth Engine
• Preparing training and validation samples for Machine Learning
• Understanding key Machine Learning concepts relevant to spatial data
Who Should Enroll
This course is ideal for:
• Geographers and environmental scientists
• GIS and Remote Sensing analysts
• Programmers and data scientists entering geospatial work
• Social scientists and geologists using spatial data
• Anyone who needs to apply Machine Learning for LULC mapping or geospatial tasks
If you expect to use modern Machine Learning algorithms for spatial classification, object-based analysis, or land cover mapping, this course will give you the tools and confidence to succeed.
Included in the Course
• Step-by-step instructions
• Datasets and QGIS project files
• Downloadable code for Google Earth Engine
• Practical exercises for each major workflow
Enroll today and unlock the full potential of Machine Learning for geospatial analysis in QGIS and Google Earth Engine.
