Drones in the Arctic—Mapping and Monitoring Coastal Marine Litter from the Skies

Observing the Arctic coasts from above & joining forces with local communities

My name is Apostolos Papakonstantinou, and I’m an Assistant Professor at the Cyprus University of Technology, where I specialize in Cartography and Geoinformatics. I am also a co-founder of SciDrones, the first spin-off company of the Aegean University, a company that designs drone-based solutions for environmental monitoring by leveraging Geo-AI and drones. I’ve worked extensively for more than 10 years on the use of drones to map spatiotemporal phenomena, particularly in coastal and marine environments.

SciDrones contribute to Task 2.1 – Local observations of changes within the ICEBERG project, specifically Task 2.1.1 – Automated litter monitoring. Here, our goal is to develop scalable methods to map, monitor, and quantify marine litter along Arctic coastlines using drones, time-lapse cameras, and geospatial artificial intelligence.

In the ICEBERG project, drone technology meets citizen science to unlock new possibilities for monitoring and protecting the fragile Arctic coastal environment. By observing the Arctic coasts from above, we gather high-resolution imagery critical for mapping marine litter, erosion, and ecosystem changes.

However, the true strength of this effort lies in our collaboration with local communities. Empowering residents to participate in drone flights and data collection not only enriches the scientific process but also ensures that monitoring efforts are grounded in local knowledge and priorities. This fusion of technology and community fosters a shared responsibility for environmental stewardship, turning the skies into a platform for both innovation and inclusion.

Why are we using drones in ICEBERG, and what’s their advantage?

The Arctic coastline is remote, fragile, and constantly changing. Ground-based surveys are time-consuming, resource-intensive, and often not feasible in harsh weather. Drones offer a flexible, non-invasive way to access large areas quickly, capturing high-resolution imagery without disturbing the environment.

They’re also highly adaptable: you can fly them on demand, plan automated missions, and revisit the same locations to monitor seasonal or even daily changes. Most importantly, they generate precise, georeferenced data, which is essential for understanding where litter accumulates, how it moves, and what it interacts with.

Study areas

This study focuses on selected coastal environments in Greenland and Iceland.

Specifically, beaches in Qaqortoq, Greenland, Husavik Beach, Husavik Laxá, and PCC in Iceland. These areas are significant for their unique coastal dynamics, ecological importance, and environmental challenges.

In Greenland, Qaqortoq is a southern coastal town characterized by rocky shorelines, glacially influenced landscapes, and cold-water ecosystems. The beaches here are shaped by seasonal ice cover, tidal activity, and sediment transport, contributing to a unique marine habitat. Due to its subarctic climate, coastal processes in Qaqortoq are heavily influenced by glacial meltwater and shifting ice conditions, impacting local biodiversity and sediment deposition patterns.

These study areas offer valuable insights into Arctic coastal dynamics, climate change effects, and human-environment interactions. By examining these locations, the study aims to understand the natural and anthropogenic factors shaping coastal landscapes in high-latitude environments.

How are drones used in practice, and what needs to be considered?

Before flying, we carefully plan flight paths and select coastal zones using high-resolution satellite imagery, local observations, and safety constraints. Each drone flight mission must balance battery life, altitude, resolution, and weather conditions. For example, Arctic winds or fog can ground even the best equipment, so we always need contingency plans.

In the field, we operate drones autonomously along coastal transects, typically flying at 20–meter altitude to maximize both detail and coverage. We are flying on this flight as our target is to detect and classify small plastic items, such as the plastic bottle caps or other anthropogenic litter, that are carried by currents or wind in the coastal zone.

All flights strictly followed the standardized data collection protocol developed and implemented by the SciDrones research team.

The flight paths for coastal area mapping were designed using the linear mapping method. This approach involved flying the UAS along a predefined route corresponding to the coastline of the study area, as determined by the pilot-operator.

By applying these methodologies, the study ensured high data collection standards, contributing to high-resolution environmental assessments and facilitating effective monitoring and management strategies for coastal zones and waste disposal sites.

We also install fixed-position time-lapse cameras to capture continuous imagery, especially in remote areas with big concentrations of marine litter where repeat drone flights may not be feasible.

A key consideration is minimizing disturbance to wildlife and fragile vegetation. For that reason, we follow strict guidelines and coordinate closely with local authorities and communities.

The results are promising:

• We’ve identified litter accumulation zones.
• We’ve detected and mapped individual pieces of plastic debris 3,995 to Greenland and 584 to Iceland, respectively.
• We’ve re-trained our object detection models to differentiate litter from natural materials in the arctic environment, improving the accuracy of automated monitoring.

This data forms a growing baseline that can help researchers, policymakers, and local communities track changes, target clean-up efforts, and develop more effective waste management policies.

While our AI models may “see,” classify, quantify, and map marine litter, there’s still something powerful about the visuals themselves. The preliminary results show not only the scale of the landscape but also how marine litter appears from above, sometimes subtle, sometimes shockingly evident.

Stay tuned for more

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