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Featured project: DEPRIMAP - AI-driven Mapping of Urban Deprivation and Climate Vulnerability at Global Scale
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Climate risk is not evenly distributed within cities. Motivated by this, the DEPRIMAP project develops AI and satellite data to identify where vulnerable populations live and how exposed they are to hazards across the Global South.
Featured member, March 2026: Armi Tiihonen
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Armi Tiihonen is an Assistant Professor at Chalmers University of Technology, in the Department of Mechanical Engineering. Her research focuses on the development of materials for more robust and stable solar cells, using approaches accelerated by machine learning and laboratory automation.
Machine learning based classification of tree crops of Syrian Arab Republic
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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
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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.