3-päivänen kurssi. Ota haltuun paikkatiedolle sopivat  koneoppimismenentelmät.

Practical machine learning with spatial data

• Would you like to learn how to predict land usage based on satallite images or forest class based on orthophotos? 

This course gives a practical introduction to machine learning with spatial data, both to shallow learning and deep learning models, including convolutional neural networks (CNN).

The course consists of lectures and hands-on exercises in Python. The main used libraries are scikit-learn, torchgeo and ultralytics.

The course is primarly intended for geoinformatics specialists who wish to learn how to use machine learning models with spatial data. Additionally, this course suits general data scientists who would like to use also spatial data for machine learning projects.

Deep learning models ofter require GPUs for efficient model training. During the course we will use Roihu supercomptuer for the exercises. 

Use of CSC’s supercomputers is generally free-of-charge for users from Finnish universities and state research institutes. EuroHPC LUMI is a much bigger supercomputer, but the practical usage has many similarities with Roihu. LUMI is available for academic users from EU. Additionally, companies have possibilities to use LUMI