The computing power of HPC brings new opportunities in the protection of the brown bear 

Challenge: The brown bear (Ursus arctos) is one of the most iconic yet also controversial species in our nature. In Slovakia, its population is relatively stable, but monitoring its movement and behavior is crucial both for nature conservation and for human safety. Traditional methods such as visual observation or tracking are time-consuming and often inaccurate. Modern camera traps can capture thousands of images from the forest, but evaluating them manually is practically impossible.

Solution: A team of researchers from the Faculty of Natural Sciences and Informatics at UKF in Nitra developed an artificial intelligence system aimed at automatically recognizing whether a bear is present in an image or not. They used convolutional neural networks (CNNs) – the same principle applied, konvolučné neurónové siete (CNN) for example, in facial recognition on mobile phones.

For training the model, they collected:

• 4 974 images with a bear
• 656 images without a bear (other animals or empty forest)

The data were provided by the Slovak Hunting Chamber, the National Zoological Garden in Bojnice, and the State Nature Conservancy of the Slovak Republic.

Use of HPC infrastructure: Training artificial intelligence on such data is extremely computationally demanding. It requires repeated processing of thousands of high-resolution images (512×512 px), parameter tuning, and testing of different model architectures.

A regular computer would need weeks for this process. Thanks to the supercomputer and NSCC Slovakia, it was possible to:

• train the models within a few hours to days
• compare multiple approaches (ResNet, MobileNet, YOLOv8/v10)
• analyze the weak points of the model and visualize what it had “learned"

HPC enabled researchers to experiment quickly and efficiently – allowing them to move from a basic model to a methodology applicable in the future.

Results

• The model learned to recognize the basic features of the bear and achieved high accuracy during training (>90%).
• In real conditions (night shots, noise, camera movement), however, the accuracy is still insufficient for deployment in the field.

Impact and future: Although the results are not yet perfect, the research shows that artificial intelligence has great potential in nature conservation. In the future, automatic analysis of camera traps could:

• help monitor the population size and movement of bears
• reduce the risk of conflicts with humans
• save researchers hundreds of hours of manual work

The next step is to expand the dataset and use synthetic data – computer-generated images that will enrich the training database. Here too, the supercomputer will be crucial, since generating and processing such data is again highly demanding.

Thanks to the supercomputer, Slovak researchers managed to build the first step towards a system that could, in the future, facilitate the monitoring of the brown bear – a species that is part of Slovakia’s natural environment and cultural heritage.