EGU25-8287, updated on 24 Mar 2025
https://doi.org/10.5194/egusphere-egu25-8287
EGU General Assembly 2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Poster | Tuesday, 29 Apr, 10:45–12:30 (CEST), Display time Tuesday, 29 Apr, 08:30–12:30
 
Hall X1, X1.126
Building Arctic Resilience through Citizen Science and Artificial Intelligence in Marine Pollution Control
Victor Lion1, Arnab Muhuri1, Natascha Oppelt1, Apostolos Papakonstantinou2, Christine Liang3, Barbara Jóźwiak4, Adam Nawrot4, Élise Lépy5, and Thora Herrmann5
Victor Lion et al.
  • 1Department of Geography, Kiel University, Kiel, Germany
  • 2SciDrones PC, Mytilene, Greece & Cyprus University of Technology, Limassol, Cyprus
  • 3Helmholtz-Centre for Environmental Research - UFZ, Leipzig, Germany
  • 4forScience Foundation, Poland
  • 5Faculty of Humanities, University of Oulu, Oulu, Finland

The Arctic is one of the most vulnerable regions on Earth concerning climate change and is increasingly affected by pollution from human activities. The ICEBERG project (Innovative Community Engagement for Building Effective Resilience and Arctic Ocean Pollution-Control Governance in the Context of Climate Change) is a multidisciplinary initiative funded by the European Union. It focuses on assessing types, sources, distributions, and impacts of pollution on ecosystems and coastal communities across the European Arctic. Case studies in West Svalbard, South Greenland, and North Iceland are being used to develop community-driven strategies to enhance resilience and reduce pollution. The project addresses a range of pollutants, including macro-, micro-, and nanoplastics, ship emissions, sewage, persistent organic pollutants, and heavy metals. 

As part of ICEBERG, our team from the Earth Observation and Modelling (EOM) group at Kiel University deployed time-lapse cameras to monitor the accumulation of marine litter along Arctic beaches. Using machine learning, we aim to automate the detection and classification of marine litter, offering new insights into its types, sizes, and seasonal variations. The results will be combined with drone-based data and coastal marine observatory artificial intelligence processing, which aims to map and monitor the spatiotemporal trends of marine litter in specified areas. By leveraging the high temporal mapping capabilities of small drones with machine learning algorithms, combining both will offer a comprehensive and advanced method for mapping marine litter across various spatial and temporal scales.

In the initial phase of ICEBERG, we deployed an autonomous camera system in West Svalbard to collect year-round data from an uninhabited site, while we held community consultation meetings in Iceland and Greenland to introduce the project and jointly explore opportunities for citizen science collaborations. By adopting a citizen science approach, we are actively partnering with academic & non-academic actors, including local and Indigenous stakeholders and non-governmental organizations in Iceland and Greenland who are supporting the installation and maintenance of the cameras. Additionally, through partnerships with high school teachers and students, we are also engaging young people to raise awareness of ongoing pollution challenges and explore actionable measures for mitigation and adaptation. By developing an interactive data-sharing platform, citizen scientists have the opportunity to upload their observations of any kind of pollution, serving as data crowdsourcing along with the data from the time-lapse cameras and drones. ICEBERG empowers communities to actively contribute to the process of identifying pollution sources, monitoring coastal litter, and developing meaningful interventions. 

We will present our innovative approach for monitoring pollution on Arctic beaches, emphasizing the role of community engagement and potential future co-created solutions. By integrating artificial intelligence tools and fostering local collaborations, ICEBERG offers a sustainable and inclusive approach for addressing environmental challenges in vulnerable Arctic regions. Our presentation will highlight the use of citizen science to enhance Arctic resilience and governance, share preliminary time-lapse data from Svalbard and Iceland, and explore the opportunities and challenges of community engagement in Arctic environmental monitoring.

How to cite: Lion, V., Muhuri, A., Oppelt, N., Papakonstantinou, A., Liang, C., Jóźwiak, B., Nawrot, A., Lépy, É., and Herrmann, T.: Building Arctic Resilience through Citizen Science and Artificial Intelligence in Marine Pollution Control, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8287, https://doi.org/10.5194/egusphere-egu25-8287, 2025.