From Bones to Bytes

The digital acquisition of animal bone remains opens up new possibilities for reproducible computational analyses in archaeozoology. This thesis combines computer science, morphometrics, and archaeozoology in a unified framework spanning tabular osteometric data, landmark-based morphology, and landmark-free three-dimensional shape analysis. Using sheep and gazelle bones from modern and archaeological populations, it investigates sex-related variation, automated landmarking, and inter-species discrimination under conditions typical of archaeological material. The first part analyses osteometric measurements using machine learning methods to classify and explore sex-related structure in bone data. Supervised approaches generally performed well, with support vector machines proving the most robust under small-sample conditions, whereas unsupervised methods were primarily useful for exploratory analysis. The second part focuses on automated landmarking. Heuristic approaches based on image and mesh processing proved insufficient, whereas deep learning methods were markedly more effective. Mask R-CNN achieved good localisation accuracy on two-dimensional images, and an adapted multi-view landmark learning pipeline for 3D bone meshes produced promising results on unseen specimens, approaching or even surpassing human repeatability despite limited training data. The third part evaluates landmark-free approaches to three-dimensional shape comparison and classification. Several representations captured meaningful species-level differences between sheep and gazelle tali, while the newly proposed signed distance field descriptor (SDFD) emerged as a strong balance between performance, runtime, and interpretability. In summary, this thesis demonstrates that computational methods can meaningfully augment archaeozoological analysis across multiple levels of digital representation. Rather than replacing domain expertise, they serve as reproducible and scalable support tools that help formalise, accelerate, and extend traditional morphological research, thereby contributing to the development of morphoinformatics as an interdisciplinary framework for the digital analysis of animal bone remains.

Level thesis:degree_level

thesis.doctoral

Grantor dc:publisher

Christian-Albrechts-Universität zu Kiel

Year

2026

Author dc:creator

• Schüler, Nadine Sarah

Contributors dc:contributor

• Kröger, Peer
• Peters, Joris
• Renz, Matthias

• Morphoinformatics
• Morphometrics
• Data Science
• Supervised Learning
• Unsupervised Learning
• Deep Learning
• Animal Bone Analysis
• Machine Learning
• AI
• Artificial Intelligence
• Landmarking
• Shape Descriptor

Repository record source_url

https://macau.uni-kiel.de/receive/macau_mods_00008803

OAI identifier oai:identifier

oai:macau.uni-kiel.de:macau_mods_00008803