Detecting graves in GPR data: assessing the viability of machine learning for the interpretation of graves in B-scan data using medieval Irish case studies.

As commercial archaeogeophysical survey progressively shifts towards large landscape-scale surveys, small features like graves become more difficult to identify and interpret. In order to increase the rate and confidence of grave identification before excavation using geophysical methods, the accuracy and speed of survey outputs and reporting must be improved. The approach taken in this research was first to consider the survey parameters that govern the effectiveness of the four conventional techniques used in commercial archaeogeophysical evaluations (magnetometry, earth resistance, electromagnetic induction and ground-penetrating radar). Subsequently, in respect of ground-penetrating radar (GPR), this research developed machine learning applications to improve the speed and confidence of detecting inhumation graves. The survey parameters research combined established survey guidelines for the UK, Ireland, and Europe to account for local geology, soils and land cover to provide survey guidance for individual sites via a decision-based application linked to GIS database. To develop two machine learning tools for localising and probability scoring grave-like responses in GPR data, convolutional neural networks and transfer learning were used to analyse radargrams of medieval graves and timeslices of modern proxy clandestine graves. Models were c. 93% accurate at labelling images as containing a grave or no grave and c. 96% accurate in labelling and locating potential graves in radargram images. For timeslices, machine learning models achieved 94% classification accuracy. The >90% accuracy of the machine learning models demonstrates the viability of machine-assisted detection of inhumation graves within GPR data. While the expansion of the training dataset would further improve the accuracy of the proposed methods, the current machine-led interpretation methods provide valuable assistance for human-led interpretation until more data becomes available. The survey guidance tool and the two machine learning applications have been packaged into the Reilig web application toolset, which is freely available

Reimaging the Black Friary: Recent Approaches to Seeing Beyond Modern Activity at the Dominican Friary, Trim, Co Meath, Republic of Ireland

Archaeological and forensic searches for buried structural and human remains can, in some instances, be hindered by modern rubbish or rubble, often with poor data quality where ferrous objects are present, in clay soils, and/or in waterlogged areas. This study was a multi-method geophysical survey (ground-penetrating radar, electromagnetic, and gradiometry) of unexcavated areas at the Black Friary to delineate areas of anthropogenic activity and refine the standards for ground-penetrating radar survey with the intention of acquiring high resolution data as a method to maximise the potential to positively identify grave-like anomalies. The Black Friary, a Dominican Friary founded in 1263, was one of several Dominican houses founded after the order arrived in Ireland. After the dissolution of religious orders in the 16th century, the Friary was demolished and quarried. Historic quarrying has produced a thick (c. 40-60 cm) rubble layer across most of the site which is overlain by modern dumping. Despite the destruction of the Friary, it continued to hold significance within the community, as evidenced by its continued use as a burial ground throughout the post-medieval period. The Friary is situated in a semi-urban setting outside the northern medieval boundary of Trim town. The surviving ruins of the Friary present as grassy hummocks and exposed stonework

Accounting for Environmental and Anthropogenic Factors: Approaches to enhancing horizontal resolution and interpretability in geophysical surveys

Common hindrances in geophysical survey, whether small-scale or landscape, are modern rubbish, rubble, ferrous objects, groundwater, and/or high attenuation materials. Groundwater and high attenuation materials greatly reduce the maximum potential depth of investigation. In an attempt to overcome these obstacles, surveys were conducted at the Dominican Friary in Trim, Ireland, which aimed to refine standard survey protocols for achieving high-resolution data from single channel ground-penetrating radar (GPR) surveys of small areas (<1ha). Based on the surveys’ results, it was determined that, for single channel GPR surveys utilising a central antenna frequency between 250MHz and 500MHz, a 0.1m traverse interval will maximise the potential to delineate targets smaller than 2.5m2, though for time efficient surveys, a traverse interval less than 25% the size of a discrete target (where the target is at least 1.5m2) provides adequate raw data to delineate significant anomalies. It is hoped that these parameters can also be applied to multichannel systems in due course. Subsequent trials of these survey parameters were conducted at the Tràng An Landscape Complex (Ninh Binh, Vietnam) as part of the SUNDASIA Project, where stratigraphic changes and discrete areas of anthropogenic activity in response to climate change were investigated. Applications of high resolution data acquisition methods proved successful within this environment, as GPR delineated discrete changes in high attenuation soil. Ultimately, these case studies demonstrate the success of small area surveys which face environmental challenges. Further analysis of the success rate of these parameters is to be expected at sites throughout southwest England and Ireland

Approaches to Improving the Pre-Excavation Detection of Inhumations

As large scale landscape surveys continue to increase in commercial and research archaeogeophysics, there is still a markedly low ability to geophysically detect and interpret archaeological and forensic inhumations in some instances. The aim of this ongoing research project is to improve data acquisition by implementing an interactive ad hoc workflow model for determining appropriate methodologies for ground-penetrating radar (GPR) surveys, to improve data processing speed, and reduce observer error. Can the confidence of manual interpretations of GPR data be improved by adapting machine learning libraries for automatic object extraction and classification to GPR data based on a training dataset comprised of ground-truthed real GPR data and simulated GPR data

Vix and the Surrounding Area Vix, France 21400 Outline Geophysical Survey Report.

Paul Cheetham and Ashely Green were commissioned by Chargé de recherche CNRS, Université de Bourgogne to carry out a programme of geophysical survey at three sites in Vix and the surrounding area. Over 50 individual surveys were undertaken in the two week survey period utilised combinations of electromagnetic induction (EMI), electrical imaging (sometimes termed geoelectrical imaging or electrical tomography), and ground penetrating radar (GPR) techniques (not included in this report), to investigate areas of archaeological potential. The results from these surveys have improved our archaeological understanding of all three sites. Specifically we can confidently state: 1. That the structure of the “Lady of Vix” tumulus is shown to be complex in terms of either the original structure, later disturbances, or a combination of both. 2. On the river floodplain northeast of the village of Vix, a triple linear feature was defined in an area poorly defined on the magnetic gradiometry due to the depth of the archaeological remains, as well as finding evidence of associated settlement activities. 3. That the major ditch and rampart system on the eastern flank of Mont Lassois can be delineated effectively by electrical imaging techniques, making further study of the system possible using non-invasive approaches.

Artificial Intelligence, 3D Documentation, and Rock Art - Approaching and Reflecting on the Automation of Identification and Classification of Rock Art Images

Rock art carvings, which are best described as petroglyphs, were produced by removing parts of the rock surface to create a negative relief. This tradition was particularly strong during the Nordic Bronze Age (1700–550 BC) in southern Scandinavia with over 20,000 boats and thousands of humans, animals, wagons, etc. This vivid and highly engaging material provides quantitative data of high potential to understand Bronze Age social structures and ideologies. The ability to provide the technically best possible documentation and to automate identification and classification of images would help to take full advantage of the research potential of petroglyphs in southern Scandinavia and elsewhere. We, therefore, attempted to train a model that locates and classifies image objects using faster region-based convolutional neural network (Faster-RCNN) based on data produced by a novel method to improve visualizing the content of 3D documentations. A newly created layer of 3D rock art documentation provides the best data currently available and has reduced inscribed bias compared to older methods. Several models were trained based on input images annotated with bounding boxes produced with different parameters to find the best solution. The data included 4305 individual images in 408 scans of rock art sites. To enhance the models and enrich the training data, we used data augmentation and transfer learning. The successful models perform exceptionally well on boats and circles, as well as with human figures and wheels. This work was an interdisciplinary undertaking which led to important reflections about archaeology, digital humanities, and artificial intelligence. The reflections and the success represented by the trained models open novel avenues for future research on rock art

Law, the Digital and Time: The Legal Emblems of Doctor Who

This article is about time. It is about time, or more precisely, about the absence of time in law’s digital future. It is also about time travelling and the seemingly ever-popular BBC science fiction television series Doctor Who. Further, it is about law’s timefullness; about law’s pictorial past and the ‘visual baroque’ of its chronological fused future. Ultimately, it is about a time paradox of seeing time run to a time when time runs ‘No More!’ This ‘timey-wimey’ article is in three parts. The first part looks to a hazy remembered past of the legal emblem tradition as presented in Peter Goodrich’s Legal Emblems and the Art of Law to learn visual literacy and also to glimpse the essential elements of modern legality with authority, decision and violence. The second part maps how these images and icons of modern legality are manifest in the Doctor Who fiftieth year anniversary special ‘The Day of the Doctor.’ The third stage looks beyond these first order meanings to understand the chronological chaos of ‘The Day of the Doctor.’ The technicity of the image as a portal through time and space that the narrative revolves around charts the implications for the digital end of time for law.Arts, Education & Law Group, School of LawFull Tex

MBE of dilute-nitride optoelectronic devices

Molecular beam epitaxy of dilute-nitride materials has progressed a long way towards claiming its unique place as a key technology, which enables the development of new types of optoelectronics devices. This chapter begins by reviewing the technological particularities related to incorporation of nitrogen into III–V materials when using plasma-assisted molecular beam epitaxy. We then focus on describing the interplay between the growth parameters and nitrogen incorporation processes in dilute-nitride arsenides (III-N–As). Emphasis is laid on nitrogen-related growth kinetics that is accompanied by various bonding configurations and formation of several types of defects. An overview is provided also for dilute-nitride antimonides (III-N–Sb) and dilute-nitride phosphides (III-N–P). Finally, we review the growth optimisation and properties of several classes of dilute-nitride heterostructures for optoelectronics. These include uncooled long-wavelength laser diodes, SESAMs, VECSELs, enabling yellow emission by frequency doubling, and high-efficiency multijunction solar cells for concentrated photovoltaic systems