• 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 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 LUMI supercomptuer for the exercises. 

Use of CSC’s supercomputers is generally free-of-charge for users from Finnish universities and state research institutes. Also companies have possibilities to use LUMI. 

Topics of the course

• Introduction to machine learning
• Shallow machine learning models: regression and classification
• Preparing spatial data for machine learning
• Deep learning models
• Fully connected neural networks (MLP): regression and classification
• Convolutional neural networks (CNN)
• CNN based semantic segmentation of spatial raster data
• Object detection from spatial raster data
• Data preparations for object detection
• Segment-anything model with spatial raster data
• LUMI supercomputer’s GPUs and using batch jobs

Learning outcomes

After the course the participants should have the skills and knowledge needed to start applying machine learning for different spatial data analysis tasks. In addition, participants will be able to makes use of the supercomputers’ GPU resources for training and deploying their own machine learning models.

Prerequisites

• Basics of geoinformatics, vector and raster data, coordinate systems.
• Basics of Python. The course will include a fair amount of reading Python code, so you should be able to follow Python syntax. If you need to refresh your Python skills you can go through the materials of Helsinki University GeoPython course.
• Basic Linux commands: cd, ls, mv, cp, rm, chmod, less, tail, echo, mkdir, pwd. If unfamiliar, take a look for example at LinuxSurvival first two modules.

Location Innovation Hub’s GeoAI courses provide also a good background for this course.

Practical information

This course is offered free of charge, but registration is required (deadline: 2.12). You can choose to attend the course at CSC office in Espoo or remotely. The course does not include any catering, only coffee/tea. Several lunch restaurants are in close distance.

Participants at CSC office are provided with a training PC. Online participants need own computer with Zoom, browser-based Zoom should be enough. Two screens are very recommended for online participation.

All hands-on activities of this course will be carried out with LUMI supercomputer’s webinterface, which you can access via your favorite webbrowser. For this, you do not need any additional software installed on your own computer.

For using LUMI a CSC account is needed. In case you do not have an account for CSC’s services yet, further instructions for applying for this will be provided a week before the course. 

If you later find out that you cannot attend the course, please let us know by sending an email to event-support@csc.fi so that people on the waitlist can fill your spot.

Please note that this course is intended for users affiliated with a European higher education institution, research institute or private industry. We accept only registrations done with organisational email address (not gmail, outlook, hotmail etc).