Correcting class imbalance through synthetic training data and 3D modeling for carabid pitfall trap sampling

Crowdsourced biodiversity data provide an accessible foundation for large-scale ecological monitoring, but class imbalance limits automated species identification, particularly for rare taxa. This research explores the use of synthetic training data generated from 3D models of carabid beetle museum specimens to improve detection and classification performance for underrepresented species in crowdsourced datasets. High-resolution 3D models were created to simulate variation in lighting, orientation, and background. These synthetic images were incorporated into convolutional neural network training datasets at varying synthetic-to-real ratios to assess their impact on classification accuracy. Models were evaluated using controlled pitfall-trap imagery to examine the influence of scene and specimen variability. Results show that synthetic augmentation can improve classification performance for rare taxa, though gains are not consistent across all classes. This work offers a scalable approach to enhancing automated biodiversity monitoring and highlights the potential of museum collections to support timely conservation and ecological decision-making.