Partnership between Climate AI Nordics and Klimatkollen
We are thrilled to share our partnership with Klimatkollen 🌍📈!
Read more on our partner page! Also check out this Swedish text about the partnership.
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Efficient and precise annotation of local structures in data
Event date:
Webinar with John Martinsson, RISE and Lund University. Machine learning models now help scientists analyze vast datasets across every branch of science. These models typically improve with more data and larger architectures, mainly through supervised learning. Both training and evaluation therefore rely on labeled datasets. A main challenge is scaling the data labeling effort to the volumes required, because it is costly and label quality can vary. Methods that deliver inexpensive yet accurate labels are therefore essential. This talk examines how to lower annotation cost and increase label quality when labeling local structures in data—for example, a local structure can be a sound event in an audio recording. By detecting the boundaries of such structures automatically, we let annotators focus on supplying concise textual descriptions for the content within those boundaries. In this setting we analyze a widely used labeling method for audio where fixed and equal length audio segments are labeled with presence or absence of an event class. We benchmark it against an oracle method that defines an upper bound, and propose adaptive labeling techniques that achieve higher‑quality labels for the studied datasets at a lower cost.
SatML for large-scale above-ground biomass estimation
Event date:
Webinar with Ghjulia Sialelli, ETH Zurich. The combination of remote sensing and machine learning has made it possible to map forest properties at an unprecedented scale and resolution. In this presentation, I will focus on the application of deep learning techniques to estimate above-ground biomass (AGB), a key metric for tracking forest carbon and ecosystem dynamics. I will begin by introducing our recently published, machine-learning-ready dataset. It features high-resolution (10m) multi-modal satellite imagery, paired with AGB reference values from NASA’s Global Ecosystem Dynamics Investigation (GEDI) mission. Key aspects include the carefully selected geographic coverage, thoughtful integration of diverse satellite data sources, and the establishment of performance baselines using standard deep learning models. Next, I will describe our ongoing efforts to build on said baselines. This was done both through feature and model engineering. I will also mention some promising yet unsuccessful approaches, highlighting some key challenges of the task at hand. Finally, I will discuss future directions, including incorporating uncertainty estimation and exploring the potential for generating a global above-ground biomass map.
Will you join the #AIWeatherQuest?
Published:
ECMWF invites AI/ML experts to improve sub-seasonal forecasts (days 19-25 & 26-31)—key for climate adaptation & planning. Open to all, any tools/datasets.
Climate AI Nordics Newsletter, March 2025
Published:
Welcome to the Climate Change AI Nordics Newsletter, March 2025! Read about recent and coming seminars, workshops, and a critical perspective of environmentally sustainable AI.