Climate AI Nordics

Climate AI Nordics

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

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events

Optimising green infrastructure for climate-resilient cities: AI-driven approaches to urban cooling

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Webinar with Abdul Shaamala, Queensland University of Technology. Green infrastructure (GI) is critical in enhancing urban resilience, mitigating heat stress, and improving environmental sustainability. However, optimising the placement and configuration of green elements such as trees, parks, and vegetative corridors, requires a data-driven approach that accounts for microclimate variations, urban morphology, and long-term ecosystem benefits. This talk explores how artificial intelligence (AI), machine learning (ML), and geospatial analysis can be leveraged to optimise GI for urban cooling and climate adaptation. Specifically, it delves into tree optimisation strategies that enhance shade provision, reduce urban heat islands (UHI), and improve outdoor thermal comfort. By utilising computational models, including optimisation algorithms and thermal analysis, cities can strategically position vegetation to maximise cooling benefits while balancing urban development needs.

Reliable conclusions from remote sensing maps

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Webinar with Sherrie Wang, MIT. Remote sensing maps are used to estimate regression coefficients relating environmental variables, such as the effect of conservation zones on deforestation. However, the quality of map products varies, and -- because maps are outputs of complex machine learning algorithms that take in a variety of remotely sensed variables as inputs -- errors are difficult to characterize. Thus, population-level estimates from such maps may be biased. We show how a small amount of randomly sampled ground truth data can correct for bias in large-scale remote sensing map products. Applying our method across multiple remote sensing use cases in regression coefficient estimation, we find that it results in estimates that are (1) more reliable than using the map product as if it were 100% accurate and (2) have lower uncertainty than using only the ground truth and ignoring the map product. Paper: https://arxiv.org/abs/2407.13659

Machine learning for Earth system prediction and predictability

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Webinar with María J. Molina, University of Maryland. Machine learning can be used for Earth system prediction, or to study the uppermost limit of prediction skill theoretically achievable given the system's initial state or other factors, otherwise known as predictability. In traditional numerical weather prediction frameworks, we solve the governing partial differential equations starting from an initial state. This initialized prediction framework usually involves three stages: 1) generating the initial conditions of the Earth system, 2) running the mathematical representation of the system on a computer forward in time, and 3) analyzing the output and converting it into a format that is useful for end users. Machine learning can be used to improve each of these individual stages, or to circumvent the three stage framework altogether, and examples of each will be given in this seminar. More time during the seminar will be dedicated to the challenges surrounding subseasonal prediction, which focuses on lead times of three to four weeks, and how we can use machine learning to both uncover potential biases in our initialized prediction systems and how we can bias-correct them.

Edge AI and IoT-Driven Water Quality Monitoring

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Webinar with Atakan Aral, University of Vienna. The integration of Edge Computing and Artificial Intelligence (i.e., Edge AI) unlocks new possibilities for remote monitoring of water quality across diverse environments. This talk will explore the dual aspects of "remote" monitoring: (1) collecting water quality data in geographically remote regions with limited energy and connectivity and (2) using connected water quality sensors to gain real-time insights remotely. By deploying low-power or energy-harvesting sensors and learning from data in close proximity to sensors, we can improve efficiency, reduce latency, and maintain learning performance in challenging environments without reliable communication and energy infrastructures. The presentation will showcase two real-world case studies in river pollution source identification and monitoring of water distribution systems.

Tech-informed nature conservation - are we there yet?

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Webinar with Abdulhakim Abdi, Lund University. In the face of a global biodiversity crisis, the ability to efficiently monitor ecological change across space and time has never been more accessible. Advances in satellite remote sensing and AI have opened new frontiers for biodiversity monitoring by offering powerful tools to analyze vast datasets, automate species detection, and improve disturbance tracking. While these technologies hold great promise, their widespread adoption faces key challenges, including data accessibility, environmental costs, model interpretability, and the need for stronger interdisciplinary collaboration. This presentation explores the opportunities and limitations of tech-driven nature conservation, assessing whether current developments are sufficient to bridge critical knowledge gaps. The integration of remote sensing with AI brings us closer to a more data-informed approach to managing the living world - but are we truly ready to harness its full potential?

Leveraging AI for Large-Scale Acoustic Biodiversity Monitoring: Insights from TABMON

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Webinar with Benjamin Cretois, Norwegian Institute for Nature Research. Advancing biodiversity monitoring is crucial for meeting the EU Biodiversity Strategy targets and addressing gaps in current ecological assessments. However, collecting data to monitor the state of biodiversity is time and resource consuming. Passive Acoustic Monitoring (PAM), in combination with AI tools offers an efficient alternative to conventional data collection practices. PAM is a non-invasive method that uses sound recorders to capture wildlife vocalizations and environmental sounds over time. It is particularly valuable for monitoring elusive or nocturnal species, such as birds, amphibians, and marine mammals, that are challenging to detect visually. The "Towards a Transnational Acoustic Biodiversity Monitoring Network" (TABMON) project is an initiative to establish a transnational passive acoustic monitoring monitoring network using autonomous acoustic sensors across four different European countries: Norway, Netherlands, France and Spain. TABMON’s objective is to demonstrate how acoustic sensing, coupled with cutting-edge AI, can complement traditional monitoring methods and support the development of methods to better monitor biodiversity. In this talk, we will also share our experiences with the deployment of acoustic recorders, data management strategies, and annotation protocols. These include managing large-scale, networked deployments across diverse landscapes, designing an efficient annotation workflow, and leveraging AI tools to process and analyze massive datasets.

Leveraging AI for Climate Resilience in Africa: Challenges, Opportunities, and the Need for Collaboration

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Webinar with Amal Nammouchi, Karlstad University and AfriClimate AI. As climate change issues become more pressing, their impact in Africa calls for urgent, innovative solutions tailored to the continent’s unique challenges. While Artificial Intelligence (AI) emerges as a critical and valuable tool for climate change adaptation and mitigation, its effectiveness and potential are contingent upon overcoming significant challenges such as data scarcity, infrastructure gaps, and limited local AI development. This talk explores the role of AI in climate change adaptation and mitigation in Africa. It advocates for a collaborative approach to build capacity, develop open-source data repositories, and create context-aware, robust AI-driven climate solutions that are culturally and contextually relevant.

Frontiers in machine learning for weather forecasting

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Webinar with Joel Oskarsson, Linköping University. Recent years have seen rapid progress in using machine learning models for weather forecasting. These models show impressive performance, matching or even outperforming existing physics-based models, while running in a fraction of the time. This is fundamentally and rapidly changing the landscape of weather forecasting today. In this talk I will discuss the factors that enabled this paradigm shift, the core machine learning methods used and the research questions at the bleeding edge of machine learning for weather. In particular I will focus on how current methods can be extended to regional and probabilistic forecasting. For regional forecasting I will showcase graph-based methods for building limited area weather forecasting models. I will also discuss how generative machine learning methods can enable probabilistic forecasting, giving much-needed estimates of uncertainty and allowing for predicting extreme weather events.

Estimation of water quality parameters using remote sensing data and machine learning models

Event date:

Webinar with Alireza Taheri Dehkordi, Lund University. The global decline in water quality, exacerbated by climate change and population growth, underscores the need for continuous and accurate monitoring of water quality parameters (WQPs). Remote sensing (RS) data, especially from multispectral satellites like Sentinel-2 and Landsat-8, offers large-scale, periodic observations for tracking WQPs. However, deriving accurate estimates solely from RS data is complex due to the intricate relationships between spectral bands and water quality indicators. This talk presents two novel machine learning approaches that leverage advanced RS data processing to enhance water quality monitoring.

Artificial Intelligence for Climate Change Mitigation

Event date:

Webinar with Alp Kucukelbir, Columbia University. Artificial intelligence (AI) has the potential to make very significant contributions to climate change mitigation. The complexity and scale of the challenge is broad. In this talk, I break down opportunities for AI to effect incremental and transformational change across multiple sectors, focusing on industries with large carbon footprints. I highlight barriers and risks to the adoption of AI, including the carbon footprint of AI usage worldwide. I focus on the multiple definitions (and ultimate importance) of "trust in AI" and its impact on the integration of AI into complex workflows. This talk is for AI practitioners looking to understand how AI fits into the bigger picture of climate change. I highlight opportunities and challenges in each sector that I hope will motivate collaboration across academia and industry.

2025 Nordic Workshop on AI for Climate Change

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The 2025 Nordic Workshop on AI for Climate Change will gather researchers from the Nordics. This one-day, in-person workshop, will take place in Gothenburg, Sweden, May 13th 2025. The workshop will feature a mix of keynotes, oral presentations, and posters around the topics of AI for climate change, including AI for biodiversity and the green transition. The workshop will be a meeting point for a wide range of researchers from (primarily) around the Nordic countries.

news

Climate AI Nordics Core Team

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We are proud to announce the amazing Core Team of Climate AI Nordics! Ten people spread over the Nordics will take place in the Core Team, to share the responsibility of developing the network and run our activities.

Featured Paper: Deep learning-enhanced detection of road culverts in high-resolution digital elevation models: Improving stream network accuracy in Sweden

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The new Climate AI Nordics Featured Paper is "Deep learning-enhanced detection of road culverts in high-resolution digital elevation models: Improving stream network accuracy in Sweden" by William Lidberg. In this work, Lidberg combines LiDAR data and aerial imagery with deep learning to detect 87 % of all road culverts in Sweden. These culverts are in turn used to increase the accuracy of maps featuring small watercourses.

Partnership between Climate AI Nordics and AfriClimate AI

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We are excited to have partnered with [AfriClimate AI](https://www.africlimate.ai/), a grassroots community dedicated to harnessing the power of AI for a sustainable, prosperous and climate-resilient Africa 🌍✨!

Upcoming seminars

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Climate AI Nordics will be hosting many seminars in 2025, featuring leading researchers exploring the role of AI in addressing climate change. Some speakers in the first quarter include Amal Nammouchi on leveraging large language models and deep reinforcement learning for trustworthy decision-making in energy management. Abdulhakim Abdi on the use of AI and Earth observation data to monitor ecosystems amid the biodiversity crisis. Atakan Aral on AI-driven environmental monitoring systems, and Sherrie Wang on AI applications in sustainable agriculture and climate mitigation. María J. Molina on AI's potential to predict extreme weather and inform climate strategies. Together uniting diverse perspectives on AI-driven climate solutions.

Featured Paper: ONEKANA: Modelling Thermal Inequalities in African Cities

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The new Climate AI Nordics Featured Paper is "ONEKANA: Modelling Thermal Inequalities in African Cities" by Sabine Vanhuysse and colleagues. This research addresses the pressing issue of thermal disparities in rapidly urbanizing African cities, where vulnerable populations are disproportionately affected by extreme heat due to environmental and socioeconomic factors.

Climate AI Nordics in Computer Sweden on how AI can help save the climate

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As COP29 is convening in Azerbajdzjan, Olof Mogren, co-founder of Climate AI Nordics was interviewed in Computer Sweden. "When it comes to climate change, we have to work broadly both with mitigation techniques and by adapting to the effects we are already seeing". "AI can be a tool to support these efforts".

Climate AI Nordics is now live!

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Climate AI Nordics is a network for researchers in the nordics working on problems related to tackling climate change using AI and machine learning. Our web site is now live.

newsletter

Climate AI Nordics Newsletter, March 2025

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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.

Climate AI Nordics Newsletter, February 2025

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Welcome to the Climate Change AI Nordics Newsletter, February 2025! Read about recent and coming seminars, workshops, and the monitoring of Baltic seabirds using AI-powered cameras and microphones.

Climate AI Nordics Newsletter, January 2025

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Welcome to the Climate Change AI Nordics Newsletter, January 2025! Read about recent and coming seminars, workshops, and the modelling of road culverts in high-resolution digital elevation models.

December 2024

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Welcome to the Climate Change AI Nordics Newsletter, December 2024! Read about recent and coming seminars, workshops, and the modelling of thermal inequalities in African cities.

November 2024

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Welcome to the Climate Change AI Nordics Newsletter, November 2024! Read about recent and coming seminars, workshops, and publications from the network's researchers.