AI Bear Radar
- Curators' Team
- Dec 17, 2025
- 5 min read
Silent Sentinels Guarding the Arctic Frontier
As the Arctic warms faster than any other region on Earth, communities are experiencing more frequent interactions with polar bears displaced by the shrinking of sea ice. This growing challenge underscores the need for systems capable of detecting approaching bears before encounters occur.
In recent years, Geoff York, Senior Director of Science and Policy at Polar Bears International (PBI), has led the development of an AI-enabled radar system designed to remotely detect polar bears and alert residents in time to take preventive action. According to Kieran McIver, manager of Churchill Field Operations at PBI, the system must be able to distinguish between bears, humans, vehicles, and other wildlife—an essential requirement to avoid false alarms. The approach combines a radar capable of scanning large areas with AI software for object classification.
As bears adapt to a changing environment and spend more time on land due to the loss of sea ice, the risk of dangerous encounters with humans and wildlife increases significantly. Tools such as Bear-dar play a vital role in this scenario by providing early warnings that enable communities to prepare and respond appropriately. By implementing non-lethal deterrents, residents can mitigate the risks associated with bear interactions. In this rapidly changing Arctic landscape, investing in early-detection technologies is essential not just for technical advancement but also for ensuring the safety of both people and wildlife.
From Concept to Cold Reality: Building Bear-dar
Bear-dar is a medium-range radar system developed to monitor expansive open areas and accurately detect polar bears. Researchers at PBI are training their AI to recognize polar bear movement patterns and body shapes, a complex task because a bear’s radar appearance changes dramatically with its orientation. As York explains, a bear looks very different when it is facing the radar antenna than when it is moving across the scanning field. This variability poses a central challenge for practical system training. Additionally, the radar’s high sensitivity necessitates that the AI distinguish polar bears from other wildlife, ensuring precise identification across diverse environments.
Once a bear is positively identified, the AI can track its movements and send warnings via text message or email to people in the area. York explains that, after fine-tuning, the radar system will be deployed in locations where bear-human interactions are likely. Remote radar towers could be equipped with strobe lights or loudspeakers that activate automatically when a bear approaches, providing an additional deterrent.
Bear-dar is part of a broader suite of emerging tools that integrate deep learning, environmental forecasting, and community knowledge. For instance, a deep learning model created by the British Antarctic Survey and WWF partners now predicts sea-ice conditions up to three months in advance—twice the range previously possible. These forecasts help Indigenous hunters, shipping operators, and conservation planners make safer decisions. Similar advances in passive acoustic monitoring and satellite data analysis are improving visibility into Arctic ecosystems, revealing whale movements beneath sea ice and documenting long-term glacier retreat. Together, these innovations help communities and scientists respond more quickly to the region’s accelerating transformations.
A Shield for Communities and Conservation
One of the primary purposes of Bear-dar is to provide communities with sufficient time to avoid dangerous encounters. When alerts are issued, people can delay routine activities, such as having children walk to school, until it is safe. Raymond Friesen, a research support specialist at PBI, emphasizes that early detection reduces the instances in which a bear must be killed for public safety. By warning residents, Bear-dar reduces the likelihood of conflict and ultimately protects both humans and bears.
As polar bears spend increasing amounts of time on land, the risk of accidental encounters rises. Communities need additional safety tools to manage this shift, and early-warning systems are among the most practical solutions. With advance notice, individuals can deploy nonlethal deterrents—such as noisemakers or flares—to redirect bears away from populated areas. This proactive approach helps ensure that bears do not become habituated to human settlements, thereby reducing long-term risks.
To test the system in real-world conditions, PBI deployed Bear-dar near Churchill, Manitoba—a location where large numbers of polar bears gather each fall as they wait for the sea ice to return. Churchill already hosts PBI’s Polar Bear Cams and Tundra Connections® webcasts, providing infrastructure for testing. Community support has been strong, with residents recognizing the potential safety benefits. These tests help refine the system and ensure it can respond to the unpredictable behavior of wild bears.
Field Testing and the Challenge of the Unseen
Testing the radar in Churchill revealed some of the challenges of tracking polar bears. As York explains, a bear may lie down for minutes or even days, causing the radar signal to disappear. To address this, the team conducted additional trials at the Assiniboine Park Zoo in Winnipeg, where the radar could monitor bears under more controlled conditions. Under McIver’s supervision, the system collected data that would otherwise be difficult to capture in the wild, helping improve the AI’s ability to recognize variations in bear behavior.
This work fits within PBI’s broader innovation vision: technology must serve a clear conservation purpose and should never be pursued merely for novelty. In practice, this means combining scientific tools with local expertise and aligning technological development with community needs. Technology, in this framework, is meant to amplify human knowledge—not replace it.
While many environmental AI systems are being developed—from deep-learning models that analyze sea ice to acoustic tools that detect whales or algorithms that monitor glacier changes—Bear-dar stands out because it addresses a direct and urgent safety issue. These other projects illustrate the broader growth of AI in environmental research, but Bear-dar’s purpose is more specific: providing early warnings when polar bears approach communities.
By focusing on real-time detection rather than large-scale environmental patterns, the system fills a gap in existing Arctic monitoring technologies. Unlike many research dashboards designed to visualize climate trends, Bear-dar is intended to operate as a practical, on-the-ground tool that can help protect people before an encounter occurs.
The Future of AI Wildlife Monitoring
York emphasizes that conservation organizations have a responsibility to support people who share their environment with large predators. Building tools that enhance coexistence is central to PBI’s mission, and Bear-dar reflects this commitment by addressing real safety concerns with practical technological solutions. By providing communities with early warning of approaching bears, the system fosters safer interactions and reduces the likelihood of conflict.
McIver and Friesen note that the underlying technology is not limited to polar bears. With proper training, similar systems could be adapted to track other species or support conservation research in different regions. They envision a future in which radar units are permanently installed in northern environments to provide year-round monitoring of bear movements.
Ultimately, the Bear-dar project illustrates how AI and radar technology can be integrated into conservation strategies grounded in local knowledge and community partnerships. As Arctic conditions continue to evolve, tools like these will be essential for helping people and wildlife coexist safely across a landscape undergoing rapid transformation.
