Seminar – Machine Learning with spatial data

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Are you working with geospatial data (raster, vector, point cloud) and machine learning? Join our seminar to share and learn best practices, discuss and network. 

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The aim of this seminar is to share best practices of Machine Learning with spatial data and to exchange experiences and ideas among spatial Machine Learning researchers and practicioners in Finland. This will also give us at CSC the opportunity to learn current and potential users.

The idea is to concentrate on the technical side of Machine Learning with spatial data:

– Data preparation,
– tools,
– solutions and
– challenges.
The only prerequisite for participation is to have an interest in Machine Learning with spatial data. We also encourage companies to participate to learn about the possibilities of LUMI supercomputer for spatial machine learning. Experience of using CSC services in not mandatory, but of course can be helpful. We want this to be an open and welcoming event; especially for asking questions (“there are no stupid questions”).

The seminar is open and free of charge to everyone interested. You can participate to present your own work or just listen in (Please choose the appropriate option during registration). You will get the most out of this seminar by attending in person in Keilaniemi, but we will also provide the possibility to listen to the talks online.

The event will provide the possibility for researchers and machine learning practicioners to share and discuss their work. CSC specialists will host the meeting, briefly tell about CSC resources for Machine Learning with spatial data and be available for questions.

List of currently confirmed talks: 

• Vector ML – case Gaze-Aware Interactive Map System (Pyry Kettunen, FGI)
• Deep Learning based Semantic Segmentation Workflow for the QGIS Plugin EnMAP-Box (Leon-Friedrich Thomas, Uni Helsinki)
• Monitoring Bark Beetle Damage: Technical Insights into UAS Image Classification with Deep Neural Networks (Emma Turkulainen, FGI)
• Detecting soil cover on arable land in winter from satellite remote sensing ( Maria Yli-Heikkilä, LUKE)
• Deep learning for estimating 3D vegetation traits from multispectral UAVs (Jani Kuurasuo, University of Helsinki)
• Hyperspectral superresolution and multi-image fusion for remote sensing forest studies (Jorma Laaksonen, Aalto University)
• 3-D modeling of biodiversity and ecosystem services (Parvez Rana, LUKE)
• A New Approach for Feeding Multispectral Imagery into Convolutional Neural Networks Improved Classification of Seedlings (Mohammad Imangholiloo, University of Helsinki)
• Your talk here?

Tentative schedule:

12:00 – 13:30

• Introduction round
• Welcome and practicalities
• Project presentations, lightning talks and longer talks; TBD

13:30-14.00 Break

14:00 – 15:30

• Project presentations, lightning talks and longer talks; TBD
• Discussion around good practices for Machine Learning with geospatial data