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

Since launching in October 2024, our community has grown to 177 members across the Nordic region and 61 international affiliates. 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, collaborations, and opportunities—including a spotlight on a new survey of Bayesian optimisation for climate challenges and details about the upcoming SWEO2025 Earth observation event in Stockholm.

News

Featured paper: Bayesian optimisation against climate change: applications and benchmarks

2025-08-25 The featured paper this month shows how Bayesian optimisation can support climate change mitigation by tackling optimisation problems in renewable energy and environmental monitoring. This includes a review on applications across four main use cases—material discovery, wind farm layouts, optimal renewable control, and environmental monitoring, and the proposal of benchmark problems to guide future research. The work is connected to ongoing projects at Climate AI Nordics, such as efforts to design safer solar panel materials and improve air pollution monitoring.
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Climate AI Nordics represented at AI+Environment Summit!

2025-08-14 Francesca Larosa, PhD, core team member at Climate AI Nordics, will be a session panelist at AI + Environment Summit Zurich in October! Be sure to check out this exciting event and its great lineup of speakers!
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Featured member

Featured member, August 2025: Miki Verma

2025-08-14 Miki is the Lead Data Scientist at Rewiring America, the leading U.S. nonprofit dedicated to the electrification of American homes. She develops data-driven tools, research, policy, and narratives to help facilitate households’ transition from fossil-fuel-powered to efficient electric systems. Her recent work includes leading the development of the Residential Electrification Model, a free API that predicts bill savings, emissions reductions, and energy impacts of electrification upgrades at any U.S. address.
(Read more)

Coming events

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

Event date: 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.
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SWEO2025: Earth observation event in Stockholm

Event date: 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.
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Recent events

Short course on artificial intelligence for environmental data

This event took place 2025-09-17 to 2025-09-18. This two-day short course introduces early-career researchers and professionals to how AI can be applied for processing and interpreting environmental data, with a focus on climate-related analysis and earth observation. Participants will learn core AI methods—including Large Language Models (LLMs)—and gain hands-on experience with practical tools through guided exercises.
(Read more)

Generative domain adaptation and foundation models for robust Earth observation

This event took place 2025-09-11. Webinar with Georges Le Bellier, CNAM. Deep learning for remote sensing plays a crucial role in turning satellite and aerial imagery into dependable, real-world insights. However, Earth observation models must handle diverse environments, sensors, and conditions—such as clouds, seasonal shifts, and geographic differences—while still producing accurate results. In this talk, we explore two paths that lead to more robust and adaptable algorithms: generative domain adaptation and geospatial foundation models. First, I will introduce FlowEO, a generative approach of Unsupervised Domain Adaptation (UDA) for Earth observation, and show its high performance in UDA scenarios for several downstream tasks, including dense prediction and classification. This flow-matching-based translation method improves pretrained predictive models' accuracies in challenging scenarios such as post-disaster response and high cloud coverage cases with SAR-to-optical translation. FlowEO’s generative domain adaptation method is independent of the downstream task and does not require retraining the predictive model. Then, I will present “PANGAEA: A Global and Inclusive Benchmark for Geospatial Foundation Models“, a standardized evaluation protocol that covers a diverse set of datasets, dense prediction tasks, resolutions, sensor modalities, and temporalities. This benchmark includes comparison between geospatial foundation models but also with supervised baselines, namely U-Net and ViT, and highlights the strengths and weaknesses of GFMs. In addition, PANGAEA evaluates models’ accuracy in cases where labels are limited and questions the impact of multi-temporal data for GFMs.
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ESA Phi-Lab Sweden Opening Event

This event took place 2025-08-26. Welcome to the official opening of ESA Phi-Lab Sweden in Stockholm. The opening ceremony will take place on August 26, starting with a presentation of the Phi-Lab’s mission and thematic focus, followed by a symbolic ribbon-cutting ceremony. After a light lunch, the afternoon continues with a conference programme featuring inspiring presentations, forward-looking discussions, and a poster session for sharing contributions (pre-registration required). We welcome researchers, start-ups, industry players and the public sector to join this important step towards a future where Sweden plays an active role in shaping AI-driven space applications.
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Norway launches six new AI research centers

This event took place 2025-08-26. The Norwegian government has announced six new national research centers for artificial intelligence, backed by over NOK 1 billion over five years. While their main missions range from education to robotics, creativity, and decision-making, sustainability, ethics, and environmental responsibility are important themes in several. Many PhD positions on relevant topics will be announced in the coming year. The centers will be presented in a public online event on August 26.
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Job openings

Doctoral student in Water Resources Engineering with a focus on GeoAI and Remote Sensing in Hydrology

Lund University is hiring a doctoral student in water resources engineering with a focus on GeoAI and remote sensing in hydrology
(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.