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This study introduces a multimodal framework that integrates convolutional neural networks with osteometric measurements to classify avian skeletal remains, addressing a gap in zooarchaeological applications of AI. Utilizing a dataset of over 10,000 images, the model achieved 86% accuracy in skeletal element identification and 51% top-1 accuracy in family-level classification, with a notable 75% top-3 accuracy. These findings highlight the potential for combining visual and morphometric data to enhance the identification of bird bones in archaeological contexts.
Achieving 86% accuracy in classifying avian skeletal elements reveals the untapped potential of AI in zooarchaeology.
Artificial intelligence has shown considerable potential for archaeological applications, yet its use in zooarchaeology remains limited, particularly for the identification of avian skeletal remains. This study presents a proof-of-concept multimodal framework that integrates convolutional neural network-based image analysis with osteometric measurements for the classification of bird bones. Using a dataset of more than 10,000 images from multiple museum and research collections, two classification tasks were investigated: skeletal element identification and family-level taxonomic classification. Prior to classification, images were automatically segmented using a two-stage pipeline combining BiRefNet and SAM2. Visual features extracted with a pre-trained EfficientNet_V2_S backbone were fused with standardized morphometric data through a feature-level multimodal architecture. The model achieved 86% accuracy on the test set for bone-type classification, demonstrating reliable recognition of skeletal elements. Family-level classification proved more challenging, reaching 51% top-1 accuracy but 75% top-3 accuracy, indicating that correct taxa were frequently included among the most probable predictions. These results demonstrate the feasibility of combining visual and morphometric information within a unified deep-learning framework and establish a methodological baseline for future AI-assisted zooarchaeological identification. The approach contributes to ongoing efforts to develop scalable, interpretable, and archaeologically meaningful tools for the study of avian remains.