Machine learning arrives in archaeology

SH Bickler - Advances in Archaeological Practice, 2021 - cambridge.org
Machine learning (ML) is rapidly being adopted by archaeologists interested in analyzing a
range of geospatial, material cultural, textual, natural, and artistic data. The algorithms are …

UFS-Net: A unified flame and smoke detection method for early detection of fire in video surveillance applications using CNNs

A Hosseini, M Hashemzadeh, N Farajzadeh - Journal of Computational …, 2022 - Elsevier
Fire is a recurring event that usually causes a lot of social, environmental, ecological, and
economic damage in different environments. Therefore, machine vision-based fire detection …

Convolutional neural networks for archaeological site detection–Finding “princely” tombs

G Caspari, P Crespo - Journal of Archaeological Science, 2019 - Elsevier
Creating a quantitative overview over the early Iron Age heritage of the Eurasian steppes is
a difficult task due to the vastness of the ecological zone and the often problematic access …

Artificial intelligence provides greater accuracy in the classification of modern and ancient bone surface modifications

M Domínguez-Rodrigo, G Cifuentes-Alcobendas… - Scientific Reports, 2020 - nature.com
Bone surface modifications are foundational to the correct identification of hominin butchery
traces in the archaeological record. Until present, no analytical technique existed that could …

Automatic taxonomic identification based on the Fossil Image Dataset (> 415,000 images) and deep convolutional neural networks

X Liu, S Jiang, R Wu, W Shu, J Hou, Y Sun, J Sun… - Paleobiology, 2023 - cambridge.org
The rapid and accurate taxonomic identification of fossils is of great significance in
paleontology, biostratigraphy, and other fields. However, taxonomic identification is often …

LUWA Dataset: Learning Lithic Use-Wear Analysis on Microscopic Images

J Zhang, I Fang, H Wu, A Kaushik… - Proceedings of the …, 2024 - openaccess.thecvf.com
Abstract Lithic Use-Wear Analysis (LUWA) using microscopic images is an underexplored
vision-for-science research area. It seeks to distinguish the worked material which is critical …

Deep learning and taphonomy: high accuracy in the classification of cut marks made on fleshed and defleshed bones using convolutional neural networks

G Cifuentes-Alcobendas, M Domínguez-Rodrigo - Scientific reports, 2019 - nature.com
Accurate identification of bone surface modifications (BSM) is crucial for the taphonomic
understanding of archaeological and paleontological sites. Critical interpretations of when …

Deep learning identification of anthropogenic modifications on a carnivore remain suggests use of hyena pelts by Neanderthals in the Navalmaíllo rock shelter (Pinilla …

A Moclán, M Domínguez-Rodrigo, R Huguet… - Quaternary Science …, 2024 - Elsevier
The identification of anthropogenically-modified carnivoran bones in archaeological sites is
rare in Pleistocene contexts, especially in the most ancient periods. Neanderthal groups …

Cruel traces: Bone surface modifications and their relevance to forensic science

CP Egeland, TR Pickering - Wiley Interdisciplinary Reviews …, 2021 - Wiley Online Library
The reconstruction of perimortem and postmortem events is of critical importance to criminal
investigations. In many cases, the information required for these reconstructions can be …

A hybrid geometric morphometric deep learning approach for cut and trampling mark classification

LA Courtenay, R Huguet, D González-Aguilera… - Applied Sciences, 2019 - mdpi.com
Featured Application Cut mark identification and analysis is a fundamental component for
archaeological investigation. Cut mark analysis, however, has been the root of great …