Registration is open for the 2026 Nordic Workshop on AI for Climate, which will be held in Copenhagen on June 26th.
Workshop webpage with more info: https://climateainordics.com/events/2026-nordic-workshop
Additional information on additional speakers & overall schedule coming soon!
Event date:
Webinar with Solmaz Khazaei, KTH Royal Institute of Technology. Flood is the most common natural disaster in the world, and can have catastrophic impacts on human society and the environment, including infrastructure damage, agricultural losses, and casualties, resulting in widespread economic and social disruptions. In early studies, water body detection relied on on-the-spot investigation, hydrological models and common remote sensing techniques that face issues like slow processing and real-time delays. By addressing this challenges we propose a novel hybrid PoLSAR-metaheuristic-DL models and high-resolution remote sensing data to generate accurate and rapid flood mapping for one of the huge recent flood in France. Compared with standard synthetic aperture radars (SAR), polarimetric synthetic aperture radar (PolSAR) is an advanced technique of SAR remote sensing. So, by using polarimetric decomposition methods, features were extracted and feature selection problem, one of the most challenging, was solved by using metaheuristic techniques. The selected features fed into three deep learning-based segmentation models- U_Net_V3, Nested_UNet and Efficient_UNet. The reliability of the generated flood maps was evaluated using Accuracy, precision and recall metrics. Our experimental results indicate that Nested_UNet integrate with optimized PolSAR data achieves the highest segmentation performance, with an accuracy of 0.910, precision of 0.914, and recall of 0.909. These findings underscore the capability of Nested_UNet, demonstrates superior feature extraction abilities, making it a promising choice for real-time flood segmentation applications. Moreover, detecting the knowledge of flooded areas, officials can actively adopt steps to reduce the potential impact of flood, ensure the sustainable management of natural resources and mitigate flood impacts.
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When deploying reinforcement learning-based systems for adaptive control of distributed energy systems often ignore physical limits, such as battery discharge limits, thermal comfort limits, or grid import thresholds. SafeCityLearn introduces the first benchmark to ensure reinforcement learning agents respect safety constraints while optimizing for sustainability.
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Miguel Nobre da Costa is a PostDoc at the Technical University of Denmark. His research focuses on AI-driven decision support for climate adaptation in cities and transport systems.
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The Norwegian University of Life Sciences is hiring two PhD candidates for developing machine learning models to map forest ecosystems and advance sustainable forest management.
