Climate AI Nordics

Climate AI Nordics

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

Two new Nordic networks/centers related to biodiversity, sustainability, AI


During 2026, two new Nordic networks/centers related to biodiversity, sustainability, AI will be initiated, with funding from Nordforsk:

Nordic BiodiversityAI Network: Strengthening Wildlife Animal Monitoring through Image-based Automation, led by Junwen Guo, Umeå University (Junwen is also a member of Climate AI Nordics).

NextGen Nordic Geotechnics: Bridging Sustainability, Engineering and Emerging Research Fields, led by Stefan Ritter, Norwegian Geotechnical Institute (Stefan is also a member of Climate AI Nordics).

Both of these networks include topics which partially overlap with and are included within Climate AI Nordics: AI for biodiversity/wildlife monitoring (e.g., 78 members have “Biodiversity” as one of their keywords on our people page, as of Feb 2026), and AI for tackling climate and sustainability challenges (e.g., 86 members have “Climate Modeling”, and 63 have “Sustainable Cities”, as a keyword as of Feb 2026).

As such, we at Climate AI Nordics look forward to future collaborations with these to-be-launched networks, and are excited to see more Nordic activities and initatives within these critically important areas!

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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.