Die Marschen der Insel Föhr und der Wiedingharde, Kreis Nordfriesland: eine siedlungsarchäologische Studie

Die Arbeit stellt das Siedlungsgeschehen einer siedlungsarchäologisch bisher wenig beachteten Landschaft an der Nordseeküste dar. Ergebnisse von Ausgrabungen einschließlich des umfangreichen Fundgutes werden besonders im Hinblick auf den Einfluss des Meeres und der Austauschbeziehungen entlang der Küste dargestellt

The prelude to industrial whaling:Identifying the targets of ancient European whaling using zooarchaeology and collagen mass-peptide fingerprinting

Taxonomic identification of whale bones found during archaeological excavations is problematic due to their typically fragmented state. This difficulty limits understanding of both the past spatio-temporal distributions of whale populations and of possible early whaling activities. To overcome this challenge, we performed zooarchaeology by mass spectrometry on an unprecedented 719 archaeological and palaeontological specimens of probable whale bone from Atlantic European contexts, predominantly dating from ca 3500 BCE to the eighteenth century CE. The results show high numbers of Balaenidae (many probably North Atlantic right whale (Eubalaena glacialis)) and grey whale (Eschrichtius robustus) specimens, two taxa no longer present in the eastern North Atlantic. This discovery matches expectations regarding the past utilization of North Atlantic right whales, but was unanticipated for grey whales, which have hitherto rarely been identified in the European zooarchaeological record. Many of these specimens derive from contexts associated with mediaeval cultures frequently linked to whaling: the Basques, northern Spaniards, Normans, Flemish, Frisians, Anglo-Saxons and Scandinavians. This association raises the likelihood that early whaling impacted these taxa, contributing to their extirpation and extinction. Much lower numbers of other large cetacean taxa were identified, suggesting that what are now the most depleted whales were once those most frequently used.</p

Hyperbola Detection with RetinaNet and Comparison of Hyperbola Fitting Methods in GPR Data from an Archaeological Site

Hyperbolic diffractions in Ground Penetrating Radar (GPR) data are caused by a variety of subsurface objects such as pipes, stones, or archaeological artifacts. Supplementary to their location, the propagation velocity of electromagnetic waves in the subsurface can be derived. In recent years, it was shown that deep learning tools can automatically detect hyperbola in radargrams using data measured over urban infrastructure, which are relatively clear. In contrast, in this study, we used an archaeological dataset with diverse underground structures. In the first step we used the deep learning network RetinaNet to detect hyperbola automatically and achieved an average precision of 0.58. In the next step, 10 different approaches for hyperbola fitting and thus velocity determination were applied. The derived information was validated with manually determined velocities and apex points. It was shown that hyperbola extraction by using a threshold and a column connection clustering (C3) algorithm followed by simple hyperbola fitting is the best method, which had a mean velocity error of 0.021 m/ns compared to manual determination. The average 1D velocity-depth distribution derived in 10 ns intervals was in shape comparable to the manually determined one, but had a systematic shift of about 0.01 m/ns towards higher velocities