Welcome to the October edition of the Climate AI Nordics Newsletter!

Since launching in October 2024, our community has grown to 195 members across the Nordic region and 67 international supporters. Together, we connect researchers and practitioners working at the intersection of artificial intelligence and climate action—spanning mitigation, adaptation, and environmental monitoring.

If you know colleagues in academia, public agencies, or industry who share these interests, invite them to join us at climateainordics.com/join.

This month’s issue features community updates, job opportunities, and our featured member.

News

Celebrating one year with Climate AI Nordics!

2025-10-24 From pioneering AI models for weather and biodiversity to our first Nordic Workshop on AI for Climate Change, the Climate AI Nordics community has grown and thrived throughout its first year of existance. Here’s a look back at key events and milestones from the first year.
(Read more)

Featured member

Featured member, October 2025: Signe Riemer-Sørensen

2025-10-24 Signe focuses on using AI to improve efficiency and reduce resource waste, collaborating with industries on applications such as predictive maintenance and route optimization. As a Senior Research Scientist at SINTEF, she leads the Analytics and AI group and co-leads the Norwegian Center for AI in Decisions, working with over 50 partners to advance research that helps society make better, data-informed decisions.
(Read more)

Coming events

Workshop on AI for Climate and Conservation (AICC) at EurIPS 2025

Event date: 2025-12-06 or 2025-12-07 (TBD).

Climate AI Nordics is excited to announce that the Workshop on AI for Climate and Conservation (AICC) has been accepted for EurIPS 2025! The AICC workshop will take place in Copenhagen, Denmark, Dec 6th or 7th (TBD; workshops are co-located with EurIPS)!
(Read more)

International Conference: Climate Impacts in a Changing World 2026

Event date: 2026-03-09 to 2026-03-11.

The Swedish Centre for Impacts of Climate Extremes (CLIMES) invites abstract submissions for the international conference Climate Impacts in a Changing World 2026, held in Uppsala on March 9–11, 2026. The event fosters interdisciplinary dialogue on the wide-ranging impacts of climate extremes on human and natural systems.
(Read more)

Workshop on Advances in Representation Learning for Earth Observation (REO) at EurIPS 2025

Event date: 2025-12-06 or 2025-12-07 (TBD).

The REO workshop will take place in Copenhagen, Denmark, Dec 6th or 7th (TBD; workshops are co-located with EurIPS)!
(Read more)

Recent events

Self-supervised pre-training for glacier calving front extraction from synthetic aperture radar imagery

This event took place 2025-10-23. Webinar with Nora Gourmelon, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). The factors influencing melt at glacier fronts facing the ocean remain an active area of research. To better understand these factors, it is essential to monitor changes in glacier calving fronts. Due to the importance of weather-dependent and seasonal variations, Synthetic Aperture Radar (SAR) is the preferred imaging method for such monitoring. In recent years, deep learning models have been developed to automate the extraction of calving front positions, however, their performance on SAR data remains suboptimal. Limited labeled data and high variability in SAR images hinder traditional supervised learning. Foundation models pre-trained on large, diverse datasets could provide robust feature representations that require minimal labeled data for fine-tuning on specific SAR tasks. However, in preliminary experiments, we found that the domain gap is too large for the task of extracting calving fronts from SAR imagery, and foundation models fine-tuned on the "CAlving Fronts and where to Find thEm" (CaFFe) benchmark dataset performed subpar. Therefore, we compiled an unlabeled dataset of Sentinel-1 SAR image sequences of Arctic glaciers, each associated with a single Sentinel-2 optical reference image. Using this dataset, we developed a novel multi-modal self-supervised pre-training strategy and applied it to pre-train a hybrid CNN-transformer model. Fine-tuning on the CaFFe benchmark showed that the pre-trained model outperforms its non-pre-trained counterpart, enabling more robust calving front segmentation and demonstrating the potential of data-efficient learning for SAR imagery.
(Read more)

Earth observation and deep learning for urban applications

This event took place 2025-10-09. Webinar with Sebastian Hafner, RISE. Urbanization is progressing at an unprecedented rate in many places around the world, making Earth observation (EO) a vital tool for monitoring its dynamics on a global scale. Modern satellite missions provide new opportunities for urban mapping and change detection (CD) through high-resolution imagery. At the same time, EO data analysis has advanced from traditional machine learning approaches to deep learning (DL), particularly Convolutional Neural Networks (ConvNets). Yet, current DL methods for urban mapping and CD face key challenges, including effectively integrating multi-modal EO data, reducing reliance on large labeled datasets, and improving transferability across geographic regions. This talk will present methods to address these challenges in urban mapping and CD, with applications such as multi-hazard building damage detection. It will also highlight the IDEAMAPS Data Ecosystem project, showing how its community-centric approach advances our understanding of urban deprivation and enables DL-based deprivation mapping.
(Read more)

Self-supervised pre-training for glacier calving front extraction from synthetic aperture radar imagery

This event took place 2025-10-23. Webinar with Nora Gourmelon, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). The factors influencing melt at glacier fronts facing the ocean remain an active area of research. To better understand these factors, it is essential to monitor changes in glacier calving fronts. Due to the importance of weather-dependent and seasonal variations, Synthetic Aperture Radar (SAR) is the preferred imaging method for such monitoring. In recent years, deep learning models have been developed to automate the extraction of calving front positions, however, their performance on SAR data remains suboptimal. Limited labeled data and high variability in SAR images hinder traditional supervised learning. Foundation models pre-trained on large, diverse datasets could provide robust feature representations that require minimal labeled data for fine-tuning on specific SAR tasks. However, in preliminary experiments, we found that the domain gap is too large for the task of extracting calving fronts from SAR imagery, and foundation models fine-tuned on the "CAlving Fronts and where to Find thEm" (CaFFe) benchmark dataset performed subpar. Therefore, we compiled an unlabeled dataset of Sentinel-1 SAR image sequences of Arctic glaciers, each associated with a single Sentinel-2 optical reference image. Using this dataset, we developed a novel multi-modal self-supervised pre-training strategy and applied it to pre-train a hybrid CNN-transformer model. Fine-tuning on the CaFFe benchmark showed that the pre-trained model outperforms its non-pre-trained counterpart, enabling more robust calving front segmentation and demonstrating the potential of data-efficient learning for SAR imagery.
(Read more)

Earth observation and deep learning for urban applications

This event took place 2025-10-09. Webinar with Sebastian Hafner, RISE. Urbanization is progressing at an unprecedented rate in many places around the world, making Earth observation (EO) a vital tool for monitoring its dynamics on a global scale. Modern satellite missions provide new opportunities for urban mapping and change detection (CD) through high-resolution imagery. At the same time, EO data analysis has advanced from traditional machine learning approaches to deep learning (DL), particularly Convolutional Neural Networks (ConvNets). Yet, current DL methods for urban mapping and CD face key challenges, including effectively integrating multi-modal EO data, reducing reliance on large labeled datasets, and improving transferability across geographic regions. This talk will present methods to address these challenges in urban mapping and CD, with applications such as multi-hazard building damage detection. It will also highlight the IDEAMAPS Data Ecosystem project, showing how its community-centric approach advances our understanding of urban deprivation and enables DL-based deprivation mapping.
(Read more)

SWEO2025: Earth observation event in Stockholm

This event took place 2025-10-23 to 2025-10-24. SWEO2025 is an event that revolves around the practical applications of Earth observation data, including within environmental monitoring and similar areas. The event takes place in Solna, Stockholm, between October 23rd and 24th. Click here for registration to the event.
(Read more)

Job openings

Researcher in Meteorological Remote Sensing

SMHI, the Swedish Meteorological and Hydrological Institute, is hiring a Researcher in Meteorological Remote Sensing with a Focus on Climate Monitoring of Clouds, Precipitation, and Radiation.
(Read more)

Postdoctoral fellow in earth observation

Lund University is hiring a Postdoctoral fellow in earth observation to study the patterns, drivers, and effects of vegetation fires and land abandonment using remote sensing and spatio-temporal analysis.
(Read more)

Master’s thesis projects in AI for Earth observation, ecology, and bioacoustics

We are looking for master’s students for four exciting Master’s thesis projects focusing on species distribution modeling with Earth observation and ML-based bioacoustic analysis. Join the RIDR group and contribute to cutting-edge research in collaboration with academic and wildlife management partners.
(Read more)

Your news in the newsletter!

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Climate AI Nordics is a network of researchers working to harness AI in tackling the climate crisis through both mitigation and adaptation.

We promote the development of AI-based tools and optimization methods that support sustainable decision-making—helping reduce emissions, restore ecosystems, and build climate resilience.