4.60 (81 reviews)
☑ 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
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.
Introduction to the course, GIS and Remote Sensing
Introduction to Remote Sensing: applications
Introduction to Remote Sensing: definition
Computer Set up for GIS analysis and GIS software on the market
Introduction to Machine Learning in GIS
Introduction to Machine Learning
On Machine Learning in GIS and Remote Sensing
Types and applications of machine learning in GIS and Remote Sensing
Supervised and Unsupervised Learning (classification) in GIS and Remote Sensing
Object detection in GIS
Segmentation and object-based image analysis (OBIA)
Prediction in GIS and deep learning for Big Data Analysis
Machine Learning in GIS: Hands-On
Random Forest supervised classification of Sentinel-2 image
Decision Trees classification of Sentinel-2 image
Segmentation of high-resolution satellite image
Final Project: Machine Learning for GIS on cloud (Google Earth Engine)
Supervised classification with Google Earth Engine
the course is very informative. The procedures explained by the instructor are clear and brief. Learnt many important things related to machine learning in GIS
Well, I will give 5 stars as a way to acknowledge Kate's effort to elaborate everything with care, and for seeking to captivate and encourage her students. The course itself is good, good teaching and good examples. This is an initial course, to understand the process. Many people look for a tutorial to proceed OBIA / GEOBIA with Qgis / OTB. I've seen questions about it on several forums, so I think it would be great if Kate could make this technique available to us :)
Great course on Machine Learning in GIS, provides sound background information as well as very useful the practical step-by-step exercises. The instructor does a great job!
I really like this course. Additionally, she teaches in the way you would like to focus your attention on this moment. The subject is incredibly attention-grabbing and most significantly informative. suggested!
Thank you for your all effort Kate. I learned a lot from the course. It was definitely an experience that opened new horizons. I hope I can see your new training sets soon.
Dear Kate, Many thanks indeed to you for your very interesting course, which I waited a too long time. I highly appreciate your valuable efforts. This course includes wealthy theoretical information on the GIS, Remote Sensing, Machine Learning, Image Segmentation, and image classification using Google Earth Engine. Looking forward to your new upcoming additional lectures, and courses as well. Thanks again.
thank you for your effort. I have learned a lot from this course. I am expecting a full course on specific topics. thank you