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AmalthAI: An Open-Source Computer Vision Platform for Cultural Heritage — arXiv:2608.13343

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.13343
License (as archived): CC0-1.0
Archived: 2026-10-19T04:55:53.309Z
SHA-256 of the archived text: 55353c6e47dcb1f6893d6035ec6a18b2631ce75ce3a6553aaf5c24e9652fc324
Stable URL of this page: https://metraipolis.com/documento/dc_4e831925a2c5

How to cite

AmalthAI: An Open-Source Computer Vision Platform for Cultural Heritage — arXiv:2608.13343 — arXiv (en), https://arxiv.org/abs/2608.13343 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_4e831925a2c5 (Metraipolis archive dc_4e831925a2c5, sha256 55353c6e47dcb1f6893d6035ec6a18b2631ce75ce3a6553aaf5c24e9652fc324)

BibTeX

@misc{dc4e831925a2c5,
  title = {AmalthAI: An Open-Source Computer Vision Platform for Cultural Heritage — arXiv:2608.13343},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.13343},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_4e831925a2c5; sha256 55353c6e47dcb1f6893d6035ec6a18b2631ce75ce3a6553aaf5c24e9652fc324},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.13343. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Computer vision (CV) and machine learning (ML) offer new tools for cultural heritage (CH) artifact analysis, but the CV/ML pipeline remains largely inaccessible to CH domain experts, who lack the background to configure, train, or assess models. We present AmalthAI, an open-source CV platform that bridges this gap, enabling non-ML CH experts to independently produce and validate archaeologically meaningful findings. The interface covers dataset management, training, and inference for classification, segmentation, and object detection, with Kubeflow and Katib handling scalable training and hyperparameter search. Grad-CAM localizes the image region behind a prediction, and a vision-language model (VLM) adds a text description of it for expert review. Since archaeological data is often state-owned or rights-encumbered and cannot leave institutional custody, AmalthAI's self-hostable deployment ensures sensitive data is kept within premises. We test the platform on an archaeological use case built on a custom dataset of clay textile imprints, where CH experts trained and validated segmentation, and classification models for hypothesis testing. We provide the implementation code at this https URL .

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-08-14 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2608.13343 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-19T04:46:30.241Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-08-14?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).