Partnership between Climate AI Nordics and Global Wetland Center
We are excited to share our partnership with Global Wetland Center 🌍✨!
Read more on our partner page!
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Featured member, September 2026: Stefanos Georganos
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Stefanos Georganos is an associate professor in Geomatics at Karlstad University, Sweden and affiliated with the Free University of Brussels, Belgium. He works at the intersection of remote sensing, machine learning, and urban geography.
Recap of the AICC-2 workshop at ECCV 2026 in Malmö
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From biodiversity monitoring and Arctic change to the future role of AI in conservation, AICC-2 showcased a diverse range of perspectives on AI for planetary impact. The workshop and accompanying social event created valuable opportunities for knowledge exchange and community building at ECCV 2026.
Save the date: The 3rd Nordic Workshop on AI for Climate
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Climate AI Nordics is happy to announce that our annual **_Nordic Workshop on AI for Climate_** returns again for a third time in 2027, this time in Oslo! Mark **April 22nd 2027** in your calendars, and stay tuned for more info to come!
Machine perception for scientific measurement from satellite imagery
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Webinar with Rangel Daroya, University of Massachusetts Amherst. In many scientific domains, the goal is not simply to classify an image or produce a visually plausible segmentation, but to estimate quantities that scientists and decision-makers can use: river width, water extent, sediment concentration, habitat change, or other physical variables. These tasks require models that are robust to distribution shift, efficient under limited supervision, and reliable enough to support downstream measurement. In this talk, I will discuss machine learning and computer vision methods for this setting, with a focus on geospatial imagery and hydrology. Scientific domains such as remote sensing expose several fundamental limitations of current vision systems. Models must operate across sensors, geographic regions, seasons, and acquisition conditions, often with limited labeled data and multispectral inputs that differ from standard natural image benchmarks. They must also detect small or thin structures, handle temporal variation, and avoid errors that may appear minor under conventional vision metrics but lead to substantial errors in scientific measurements. I will discuss three aspects that try to solve these problems: learning transferable representations that can adapt across tasks and datasets, developing data-efficient perception methods for multispectral and geospatial imagery, and translating model predictions into reliable physical measurements. I will highlight work on task transfer, satellite representation learning, river segmentation, and downstream estimation of river width and other hydrological variables. More broadly, this work aims to develop robust, transferable, and data-efficient machine perception systems that can turn large-scale satellite and scientific imagery into reliable measurements of the physical world.