Results 21 to 30 of about 2,510,518 (309)

Integration of Geophysical Survey Data for the Identification of New Archaeological Remains in the Subsoil of the Akrai Greek Site (Sicily, Italy)

Heritage, 2023
For more than a decade now, geophysical prospecting has been considered an integral part of archaeological research; the ability to quickly investigate large areas and locate objects buried in the ground without directly interacting with it is a key ...
Sabrina Grassi, Gabriele Morreale, Rosa Lanteri, Angelo Gilotti, Federico Latino, Saro Di Raimondo, Sebastiano Imposa   +6 more
doaj   +1 more source

A submerged 7000-year-old village and seawall demonstrate earliest known coastal defence against sea-level rise.

PLoS ONE, 2019
We report the results of underwater archaeological investigations at the submerged Neolithic settlement of Tel Hreiz (7500 - 7000 BP), off the Carmel coast of Israel.
Ehud Galili, Jonathan Benjamin, Vered Eshed, Baruch Rosen, John McCarthy, Liora Kolska Horwitz   +5 more
doaj   +1 more source

Model for Automatic Image Annotation Leveraging the OnTunAr Ontology

Computing Open
In this paper, we present the Automatic Annotation of Archaeological Images model. However, this model is composed of several methods for the analysis, description, and recognition of image components to improve the automatic annotation. Also, the latter
Marwa Ben Salah, Ameni Yengui, Mahmoud Neji   +2 more
doaj   +1 more source

Optimal Spectral Domain Selection for Maximizing Archaeological Signatures: Italy Case Studies

Sensors, 2009
Different landscape elements, including archaeological remains, can be automatically classified when their spectral characteristics are different, but major difficulties occur when extracting and classifying archaeological spectral features, as ...
Rosa Maria Cavalli, Simone Pascucci, Stefano Pignatti   +2 more
doaj   +1 more source

What Should We Do With These? Challenges related to (semi-)automatically detected sites and features. A note

Internet Archaeology
Recent advances in machine learning and computer vision techniques have brought (semi-)automatic feature detection within reach of an increasing number of archaeologists and archaeological institutions, including those in Finland.
Niko Anttiroiko
doaj   +1 more source

Finding the Features with LiDAR and SAR: Automated Detection of Archaeological Earthworks at Cahokia

Remote Sensing
Archaeological feature detection at complex, mixed-environment sites requires accurate, efficient methods for identifying subtle morphological signatures.
Justin M. Vilbig, Vasit Sagan, Joseph A. Jilek, Cagri Gul   +3 more
doaj   +1 more source

Manufacturing rectangular-sectioned axes in a Corded Ware culture in the light of refittings at Wilczyce 10, district Sandomierz (Poland)

Archeologické Rozhledy, 2017
Site Wilczyce is situated on Sandomierz Upland in the south of Poland. It was discovered in 1994 during a systematic archaeological survey within the project Archaeological Picture of Poland.
Tomasz Boroń
doaj   +1 more source

Playing with method: testing one approach towards identifying the places of past children

Evolutionary Human Sciences, 2021
Before approaching larger questions surrounding the role of children as agents of innovation in the past, we must first be able to confidently archaeologically identify their presence within social spaces.
Mackenzie Cory
doaj   +1 more source

RELIEF FEATURES OF LOWLAND AREAS AS INDICATORS OF ARCHAEOLOGICAL POTENTIAL

Opvscvla archaeologica, 2018
This paper presents the use of the data obtained by terrestrial topographic survey in analysis and assessment of archaeological potential of lowland areas. The analysis is based on investigation of relief features of the site Kurilovec-Belinščica, which is located in the lowland area of Turopolje region.
Sirovica, Filomena, Kudelić, Andreja, Tresić-Pavičić, Dinko   +2 more
openaire   +5 more sources

Palaeoproteomic Deconvolution of Physical and Genetic Collagen Mixtures

Advanced Science, EarlyView.
ClassiCOL_v2 uses a novel “peptide patching” framework that treats any collagen sample as a patchwork of peptide sequences derived from phylogenetically related species. This allows the algorithm to simultaneously resolve two previously intractable problems: multi‐species samples and/or database deficiencies.
Ian Engels, Tristan Dedrie, Synnøve K. Mo, Simon Van de Vyver, Thijs R. A. Vandenbroucke, Kévin Di Modíca, Jan Decher, Alice Toso, Dieter Deforce, Simon Daled, Alexandra Burnett, Grégory Abrams, Maarten Dhaenens   +12 more
wiley   +1 more source