Climate AI Nordics

Climate AI Nordics

Nordic network for collaborations around tackling climate change using AI and machine learning.

Featured member, March 2026: Armi Tiihonen


Hi Armi! Could you tell us a bit about yourself and your work?

I am an engineering physicist by training and currently work as an Assistant Professor at Chalmers University of Technology in the Department of Mechanical Engineering. My research focuses on developing materials for more robust and stable solar cells, using approaches accelerated by machine learning and laboratory automation. I also work on other types of sustainable energy materials.

Accelerating research is especially relevant in materials science because it commonly takes years to decades for promising lab concepts to reach market-ready applications, but we obviously want solutions as timely as possible to support climate change mitigation. Acceleration is a feasible target: it has been observed throughout the history of science, and today there is still a lot of untapped potential in novel machine learning and automation approaches that we can further develop!

This line of work is highly multidisciplinary and involves method development in areas such as probabilistic and active learning, as well as semi-autonomous laboratories. It also includes in-depth materials science characterizations whenever we encounter interesting phenomena or promising material candidates.

What kinds of research opportunities or collaborations are you excited to be part of in the future?

I am motivated to contribute to machine learning-assisted accelerated R&D workflows that support climate change mitigation, both in labs and in other development environments. I find it interesting to identify impactful bottlenecks in these workflows, target them directly, and try to get results more quickly. This also includes cases where research progresses from stage to another, such as from investigating materials to developing devices, as well as the translatability from one project or application to another. Machine learning-wise, I am specifically interested in causal methods at the moment.

Is there anything else you would like to share with the Climate AI Nordics network?

I have greatly enjoyed reading this newsletter and learing about all the exciting research happening in our community. Please do not hesitate to drop me a message if you feel like having a chat! You can reach me at armi.tiihonen@chalmers.se.

You May Also Enjoy

Machine learning based classification of tree crops of Syrian Arab Republic

Event date:

Webinar with Purnendu Sardar, Lund University. Accurate mapping of tree crops is vital for regional resource management, ecosystem service assessment, and the support of local livelihoods within the Syrian Arab Republic. Despite their socio-economic importance, tree crops are frequently misclassified or omitted in global and regional cropland products due to their complex spectral signatures and structural similarities to natural vegetation. This study proposes an integrated machine learning framework that combines the computational power of Google Earth Engine (GEE) with Python to enhance classification precision of tree crops across Syria. The methodology evaluates the integration of Global Ecosystem Dynamics Investigation (GEDI) LiDAR data with Sentinel-2 multi-spectral imagery to facilitate robust tree crop mapping. By utilizing GEE for the large-scale preprocessing of Sentinel-2 time-series data, the workflow generates high-dimensional, machine-learning-ready datasets that incorporate both structural and phenological variables. A Convolutional Neural Network (CNN) is subsequently trained in Python, chosen for its proficiency in processing time-series remote sensing data where temporal spectral patterns are more diagnostic than spatial textures. This approach allows the model to capture the distinct phenological cycles of various tree species, overcoming the limitations of traditional pixel-based or purely spatial classifiers. The findings underscore the efficacy of the CNN in distinguishing tree crop cover with high efficiency, demonstrating that the fusion of LiDAR-derived structural metrics with multi-temporal satellite data significantly reduces classification errors. The resulting high-resolution tree crop map provides an essential tool for sustainable agricultural planning and resource allocation in Syria.

AI in the wild: How Neural Networks Help Us Understand Our World Through Sound

Event date:

Webinar with Jeppe Rasmussen, University of Copenhagen. Bioacoustics, the study of nature’s sounds, has long been a powerful tool for studying wildlife. With the rise of artificial intelligence, particularly deep learning, the potential of this field has expanded dramatically. By applying advanced AI algorithms to bioacoustic data, researchers can now identify and monitor species with greater accuracy, even in environments where visual observation is difficult, such as dense forests or deep oceans. This capability is especially critical as we face the sixth mass extinction. AI-enhanced monitoring offers new hope for conservation by providing deeper insights into the presence, behavior, and well-being of endangered species. Beyond detection, AI also opens doors to understanding animal communication and emotional states, thanks to its ability to autonomously identify and prioritize key acoustic features. In this talk, I will present a series of case studies spanning multiple species and ecosystems to illustrate how cutting‑edge AI research can meaningfully advance our understanding of the living, complex world around us—and how these methods can help mitigate the biodiversity crisis we face and discover the surprisingly rich inner life of the animals surrounding us.

Bridging data gaps in the Global South: Harnessing AI and Earth observation in the humanitarian sector

Event date:

Webinar with Isabelle Tingzon, RISE. This talk highlights how AI and Earth Observation (EO) are transforming data availability for two global challenges: climate resilience and universal school connectivity. In the Caribbean, where extreme climatic hazards like hurricanes, floods, and landslides can devastate over 90% of built infrastructure across Small Island Developing States, comprehensive and up-to-date housing stock information is critical to enable resilient housing operations but is often incomplete, inaccessible, or completely non-existent. The “Digital Earth for Resilient Caribbean” initiative addresses these gaps by integrating EO data such as drone, LiDAR, satellite, and street-view imagery with AI workflows to classify roof types, materials, and building conditions. These efforts aim not only to enhance disaster response and recovery but also strengthen long-term resilience through government capacity building and knowledge exchange. In parallel, the presentation showcases UNICEF Giga’s mission to connect every school to the Internet by 2030. Reliable school location data is critical for planning telecommunications infrastructure, yet often inaccurate or incomplete in many low- and middle-income countries. By leveraging ML and satellite imagery, we’ve developed scalable pipelines to map schools across Africa on a nationwide scale, helping to ensure that no child is left behind in accessing digital learning opportunities. Together, these projects demonstrate how Geospatial AI can help bridge critical data gaps in the Global South, supporting both climate adaptation and inclusive education.

2026 Nordic Workshop on AI for Climate

Event date:

The 2026 Nordic Workshop on AI for Climate will gather researchers from the Nordics. This one-day, in-person workshop, will take place in Copenhagen, June 26th 2026. The workshop will feature a mix of keynotes, oral presentations, and posters around the topics of AI for tackling climate change, including AI for biodiversity and the green transition. The workshop will be a meeting point for a wide range of researchers from (primarily) around the Nordic countries.