Climate AI Nordics

Climate AI Nordics

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

Climes interdisciplinary summer school 2026

Event date: 2026-06-15 to 2026-06-18

Event location: Uppsala

The Climes Summer School 2026, held at Uppsala University in Sweden, is an interdisciplinary program designed for doctoral students, postdocs, and advanced master’s students to study the impacts of climate extremes. The curriculum features daily thematic lectures on climate system extremes, public health, and disproportionate societal impacts. A portion of the program is dedicated to Artificial Intelligence in climate-related problems, taught by Joakim Nivre and Olof Mogren. Their instruction covers deep learning and natural language processing for climate applications.

Participants apply these concepts in practical exercise sessions, where they use AI tools for the automated extraction of climate impact data from various texts. Beyond technical skills, the school offers training in “soft skills” like science communication and engaging with policymakers. The program is free to attend, though participants must generally cover their own travel and lodging unless they are awarded limited available sponsorship. Applications are due by March 22nd, 2026, and must include a motivation statement and a letter of support. The daily schedule will typically run from 9:00 to 16:00, concluding with participant presentations and a social dinner.

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The Material Cloud Film Festival

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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.