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).
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 GPU resources available at CSC High Performance Computers for training and deploying their own machine learning models.
- 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.
The course is similar to the Practical machine learning for spatial data course kept in autumn 2019 and 2020. Course exercise materials of previous course are available in Github: https://github.com/csc-training/geocomputing/tree/master/machineLearning
The new materials will be published here later.
Course organizers and lecturers: Kylli Ek, Samantha Wittke, Billy Braithwaite, Markus Koskela, Mats Sjöberg (all CSC)
- Lecture 1: Introduction to machine learning
- Exercise 1: Image segmentation using k-means with scikit-learn
- Lecture 2: Shallow machine learning models
- Lecture 3: Preparing spatial data for machine learning
- Exercise 2: Preparing vector data for regression
- Exercise 3: Shallow regression with scikit-learn
- Exercise 4: Image classification using shallow classifiers, grid search with scikit-learn
- Lecture 4: Introduction to deep learning models
- Lecture 5: Fully connected neural networks
- Lecture 6: Puhti GPUs and batch jobs
- Exercise 5: Fully connected regressor with keras
- Exercise 6: Fully connected classifier with keras
- Lecture 7: Convolutional neural networks (CNN)
- Exercise 7: Data preparations for CNN
- Exercise 8: CNN based image segmentation with keras
- Lecture 8: GIS software supporting machine learning for spatial data.
- Wrap-up and where to go from here
We will have coffee breaks also during morning and afternoon sessions. Participants at CSC are provided lunch and refreshments during coffee breaks.
All exercises will be done in CSCs supercomputer Puhti, using the Puhti web interface.
All participants will get Puhti training accounts.
Participants at CSC
The course is organized in CSC training class room, where everybody has access to a training PC.
Technical requirements, minimum:
- Web browser.
- ArcGIS or QGIS for viewing the spatial data files (GeoTiff, Shape, GeoPackage).
7.11.2022 09:00 +02:00 EET
9.11.2022 16:00 +02:00 EET
CSC Training Facilities & Online (Zoom)