SIOS training course on Artificial Intelligence in Svalbard (AI4Svalbard)

This autumn SIOS offers a training course on how to effectively use AI methods in Arctic Earth observation with special focus on research in Svalbard. The goal of the course is to teach participants the basic skills needed to use AI methods with Arctic Earth observation datasets. The training will be delivered by remote sensing experts from SIOS member institutions, international researchers, and experts from the industry.

  • Time: 5 - 9 September 2022
  • Location: Longyearbyen, Svalbard

Note: Due to the COVID-19 pandemic, the plans are not set in stone. If the pandemic is still a risk factor, the course will be delayed towards the end of September, postponed to 2023 or offered as a remote class instead of in-person. We will take the final decision on the mode of the training course (in-person or virtual) on 15 June 2022.

First round of applications (deadline: 15 April 2022)

You must be employed or be a student at a SIOS member institution (Is my institution a member of SIOS?). Read more about the application procedure below.

Second round of applications (15 April - 15 May 2022)

APPLY HERE

Target audience

The course is intended for scientists, master/Ph.D. students and technicians with no or modest experience with AI and machine learning in their research. Basic knowledge of programming languages (python) is preferable.

Course objectives

General objectives

Many scientists from SIOS member institutions have been working on research projects which would greatly benefit from the addition of AI methods. SIOS member institutions have installed various research infrastructures in Svalbard which deliver long-term data (https://sios-svalbard.org/metsis/search). SIOS-InfraNor project (https://sios-svalbard.org/InfraNor) involves installation of research infrastructures ranging from new instruments to remote sensing products. However, scientists may lack the necessary training required to make efficient use of available datasets using modern AI methods. PhD students from SIOS member institutions who are about to begin careers using remote sensing as well as in situ data as an integral part of their research projects can especially benefit from this training course.

This course will consist of following activities:

  • Theoretical lecture series
  • Hands-on, demo and practice sessions
  • Field excursion and social events
  • Mini-projects
     

Learning objectives

At the conclusion of the educational activities (5-9 Sept 2022) and the following mini-projects, you should understand the basis for spatial and temporal data analysis (focus on SIOS data and/or Earth observation data) and recognise errors in the received data as well as understand the value of information and how to share a code and collaborate on a project using a collaborative coding tool.

At the end of the course the student is able to

  • process, visualise, and manipulate specific data by using a computer program and critically assess the quality of the data;
  • understand how to apply, test, and interpret machine learning algorithms (clustering and decision trees) as methods for addressing research questions from your own/team research projects;
  • apply clustering and decision trees techniques to SIOS/own datasets;
  • apply deep learning (DL) techniques to selected/own datasets.

Participants are expected to

  • achieve basic/beginners understanding of AI in Arctic;
  • achieve understanding of AI applications in Earth observation in general;
  • be able to utilise simple methods in AI in their own research;
  • be able to define a mini-scale project using their own data or SIOS data.
Teachers

A tentative list of teachers will be published at the end of March.

Tentative timeline

Pre-training course activities

  • First round of application open: 15 March – 15 April 2022 (For SIOS members)
  • Second round of application open: 15 April – 15 May 2022 (Open to everyone)
  • Review of applications: 15 May-30 May 2022
  • Decision on selection: 7 June 2022
  • Final selections from waiting list after cancellations: 20 June 2022
  • Sharing practical information (travel, software and hardware requirements): 30 June 2022

Training course activities

Course dates: 5 - 9 September 2022 (starting Monday morning, ending Friday at lunch time, depending on flights).

Each day 9:30 -16:30 CEST with 1 hour lunch break and 2 coffee breaks.

The detailed course schedule will be published in due time.

Post Training activities

  • Mini-projects: 10 September - 10 October 2022
  • Mini-project presentation: 15 October 2022
  • Project summary and report: 1 December 2022
Application procedure

First round of applications (15 March - 15 April 2022): Open to participants from SIOS member institutions

In the first round of applications, this training course is only available for employees and students from SIOS member institutions (Is my institution a member of SIOS?) and will be free of charge. Course participants can apply for partial support for travel and accommodation (actual costs up to 5 000 NOK/participant from European/Nordic SIOS member institutions and up to 10 000 NOK/participant from Asian SIOS member institutions).

Each member institution has one place guaranteed - provided an application is received. If no application from a SIOS member institution is received, the allocated place is forfeit and may be allocated to an applicant from another institution.

Second round of applications (15 April - 15 May 2022): Open for participants from other institutions (non-SIOS members)

In this round, also participants from other institutions can apply. There are no fees for those participants, but they must cover their own expenses on travel and accommodation in Longyearbyen.

Practical information

This list will be updated.

  • Course participants are expected to bring their own laptops
  • SIOS will cover a networking dinner and all lunches/coffee breaks during the training course
Task force

This training course is organised by a task force comprised of the following members:

  • Ekaterina Kim - NTNU
  • Jie Zhang - Uppsala University
  • William Harcourt - Uni. of St. Andrews
  • Sara Aparício - Solenix for ESA/NOVA University of Lisbon
  • Shridhar Jawak - SIOS-KC