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Short Abstract

ICEBERG combines technology and community action to monitor Arctic beach litter via cameras, drones, and interactive mapping. Citizen data and AI enable automated litter detection, while geolocated observations highlight local engagement. Challenges and opportunities for public engagement are shown

Abstract

The ICEBERG project (2024-2026) takes a multidisciplinary approach to marine citizen science combining automated monitoring technologies with community-driven environmental monitoring and action across Northeast Iceland and South Greenland through three methods: time-lapse camera systems, drone-based monitoring, and an interactive online mapping platform to document beach litter in the Arctic. A network of 14 time-lapse cameras was developed and deployed in collaboration with residents, NGOs, and community groups in Iceland. These systems continuously capture visual data to feed into the development of an Artificial Intelligence (AI) model for automatic beach litter detection. Citizen scientists are central to this process: they help install and maintain the cameras, conduct field tests to train the AI, and take part in hands-on workshops where they learn how to build and use simple AI models for environmental monitoring. Community members in Iceland and Greenland have undertaken EASA-certified drone training, empowering them to conduct litter-monitoring flights. These citizen-led missions generate imagery for machine learning models and expands spatial coverage across difficult-to-reach coastlines. Citizen-generated data and drone footage are merged with machine learning algorithms in the Coastal Marine Litter Observatory for detecting, mapping, and visualizing marine litter in dynamic coastal litter distribution maps. The open-source uMap platform strengthens community involvement by enabling anyone to geolocate, describe, and share multimedia observations of marine litter, including photos and pollution records. Since summer 2024, more than 200 community entries have been uploaded, reflecting strong local engagement and a growing sense of ownership in environmental observation. These collective efforts highlight the potential of citizen-driven approaches to produce high-quality marine litter data, build local monitoring capacity, and foster sustainable stewardship of Arctic coastal ecosystems. The project also examines the challenges and opportunities of maintaining long-term, meaningful public participation in citizen science, particularly in balancing local motivations, technical skills, and data quality needs.