A view on Greater Angkor: a multi-scalar approach for investigating the Khmer forests

This paper will focus on the results of a joint international project (a partnership between the University of Sydney and the University of Venice) that develops and applies satellite remote sensing methodologies for finding and mapping unknown archaeological sites in the surroundings of Angkor, in Cambodia. Long famous for its temples, this World Heritage site is now increasingly recognized as a vast, low-density urban landscape. The project consists of using the spectral content of remotely sensed images to reveal the presence of buried sites and structures of the ancient Khmer landscape on the basis of the different spectral characteristics of the terrain and vegetation. By applying spectral analysis, the current research aims to scan vegetated and bare soil areas in order to clarify features that are ambiguous in existing maps and reveal features which would otherwise remain undetected

Integrated archaeological investigations for the study of the Greater Aquileia area

A large number of technologies, such as Geographic Information Systems (GIS), Global Positioning Systems (GPS), Remote Sensing (RS), geophysical instruments, allows nowadays for fast and reliable automated capture, management and analysis of archaeological data. Beyond the City Walls (BCW) is a landscape archaeology project based in the countryside of the Roman municipium of Aquileia (Italy) that applies and integrates these technologies for the reconstruction of peripheral settlement dynamics in antiquity, trialling concurrently tools that operate as hubs for acquisition of disparate field data

The combinatorial explosion: defining procedures to reduce data redundancy and to validate the results of processed hyperspectral images

This paper will focus on ways to manage large numbers of remotely sensed data images as results of image processing, a primary problem especially for those dealing with hyperspectral images that pose considerable issues due to the elevated number of channels. While briefly introducing the results of the application of several common image processing techniques in the target area of Aquileia (NE Italy), the current paper will discuss the necessity to define a set of procedures to reduce the number of final images to be used for visual inspection, selecting the ones that do not carry redundant information. Consequently, cross process coverage, detected traces evaluation and process validity tables will be presented and the results of their application discussed in order to provide information about how to reduce the images to a small number and be able to insure the complete coverage as regards to the detectable traces

Across Space and Time Papers from the 41st Conference on Computer Applications and Quantitative Methods in Archaeology, Perth, 25-28 March 2013

The present volume includes 50 selected peer-reviewed papers presented at the 41st Computer Applications and Quantitative Methods in Archaeology Across Space and Time (CAA2013) conference held in Perth (Western Australia) in March 2013 at the University Club of Western Australia and hosted by the recently established CAA Australia National Chapter. It also hosts a paper presented at the 40th Computer Applications and Quantitative Methods in Archaeology (CAA2012) conference held in Southampton

Editorial for special issue : 'Archaeological remote sensing in the 21st century : (re)defining practice and theory'

“Beg, borrow and steal”: in many ways, this is a strapline for archaeology as a discipline, and perhaps especially so for archaeological remote sensing [...

Stolen Heritage Multidisciplinary Perspectives on Illicit Trafficking of Cultural Heritage in the EU and the MENA Region

It is a well-known fact that organised crime has developed into an international network including very diverse actors – ranging from the simple ‘grave diggers’ to powerful and wealthy white-collar professionals – that adopt illegal practices like money laundering, fraud and forgery. This criminal system, ultimately, damages and disintegrates our cultural identity and, in some cases, fosters political corruption, terrorism or civil unrest through the transnational and illicit trafficking of cultural property. The forms of ‘ownership’ of Cultural Heritage are often indistinct, and – depending on the national legislation of reference – the proprietorship and trade of historical and artistic assets of value may be legitimate or not. Casual collectors and criminals have always taken advantage from these ambiguities and managed to place on the market items obtained by destruction and looting of museums, monuments and archaeological areas. Thus, over the years, even the most renowned museum institutions might have - more or less consciously – displayed, hosted or lent cultural objects of illicit origin. Ransacking, thefts, clandestine exports and disputable transactions are crimes that primarily affect countries that are rich in artistic and archaeological assets, but such activities do not involve just some countries. This is an international border-crossing phenomenon that starts in given countries and expands to many others. Some are briefly passed through while a handful of powerful and rich ones are the actual destination marketplaces. Drawing from the experience of the conference Stolen Heritage (Venice, December 2019), held in the framework of the H2020 NETCHER (NETwork and digital platform for Cultural Heritage Enhancing and Rebuilding) project, this edited volume focuses on illicit trafficking in cultural property addressing the issue from a multidisciplinary perspective and featuring papers authored by international experts and professionals actively involved in Cultural Heritage protection. The articles included expand on such diverse topics as the European legislation regulating import, export, trade and restitution of cultural objects; ‘conflict antiquities’ and cultural heritage at risk in the Near and Middle East; looting activities and illicit excavations in Italy; the use of technologies to counter looting practices and the publication of unprovenanced items. This collection is meant as a valuable resource to disseminate new results of the research as well as to facilitate a better understanding of the international legislation related to the protection of Cultural Heritage

AIKoGAM: An AI-driven Knowledge Graph of the Antiquities Market: Toward Automatised Methods to Identify Illicit Trafficking Networks

The longstanding illicit trafficking of archaeological artefacts has persistently presented a global issue, posing a substantial threat to cultural heritage. This paper introduces an innovative automated system that utilises Natural Language Processing (NLP), Machine Learning (ML), and Social Network Analysis (SNA) to construct a Knowledge Graph for antiquities. The objective is to offer insights into the provenance of artefacts and identify potential instances of illicit trafficking. The paper delineates a comprehensive methodology, from the ontology to the Knowledge Graph. The methodology comprises four distinct phases: the initial phase involves tailoring existing ontologies to match project-specific needs; the second phase centres on selecting appropriate technologies, and scraping and text-mining tools are designed to extract pertinent data from textual sources; the third phase centres in the creation of a robust and accurate Knowledge Graph that captures artefact provenance. The paper suggests employing NLP models, specifically utilising Named Entity Recognition (NER) techniques. These models automatically extract relevant information from the unstructured provenance texts, organising them as events to which both objects and actors participated with their locations and dates. The final phase is concerned with defining and building the Knowledge Graph. The authors explore a property graph model that distinctively represents nodes and relationships, each augmented by associated properties. Employing an SNA approach, the model is projected in multiple network levels of ownership histories (actor-object network) or actor relationships (actor-actor network). This approach reveals patterns within the antiquities market. When integrated with the authors’ recommended strategies such as crowdsourced ontology definition, collaboration with reputable organisations for quality sources, and the application of transfer learning techniques, the suggested approach holds promising implications for the protection of cultural heritage

A review of glass corrosion: the unique contribution of studying ancient glass to validate glass alteration models

Glass has been used in widespread applications within several sectors since ancient times and it has been systematically studied under different perspectives. However, its thermodynamic properties and the variety of its compositions, several aspects related to its durability and its alteration mechanisms remain still open to debate. This literature review presents an overview of the most relevant studies on glass corrosion and the interaction between glass and the environment. The review aims to achieve two objectives. On one hand, it aims to highlight how far research on glass corrosion has come by studying model systems created in the laboratory to simulate different alteration conditions and glass compositions. On the other, it seeks to point out what are the critical aspects that still need to be investigated and how the study of ancient, altered glass can add to the results obtained in laboratory models. The review intends also to demonstrate how advanced analytical techniques commonly used to study modern and technical glass can be applied to investigate corrosion marks on ancient samples

Automated detection in remote sensing archaeology: a reading list

The applications of automated object detection in remote sensing archaeology have grown considerably in the last few years. This reading list has been compiled as a contribution to consolidating current perspectives at September 2016, and in support of the preceding paper on the broader issues of human and computer vision in archaeological prospection (Traviglia et al. 2016)

Implicit neural representation for change detection

Detecting changes that occurred in a pair of 3D airborne LiDAR point clouds, acquired at two different times over the same geographical area, is a challenging task because of unmatching spatial supports and acquisition system noise. Most recent attempts to detect changes on point clouds are based on supervised methods, which require large labelled data unavailable in real-world applications. To address these issues, we propose an unsupervised approach that comprises two components: Neural Field (NF) for continuous shape reconstruction and a Gaussian Mixture Model for categorising changes. NF offer a grid-agnostic representation to encode bi-temporal point clouds with unmatched spatial support that can be regularised to increase high-frequency details and reduce noise. The reconstructions at each timestamp are compared at arbitrary spatial scales, leading to a significant increase in detection capabilities. We apply our method to a benchmark dataset of simulated LiDAR point clouds for urban sprawling. The dataset offers different challenging scenarios with different resolutions, input modalities and noise levels, allowing a multi-scenario comparison of our method with the current state-of-the-art. We boast the previous methods on this dataset by a 10% margin in intersection over union metric. In addition, we apply our methods to a real-world scenario to identify illegal excavation (looting) of archaeological sites and confirm that they match findings from field experts.Comment: Main article is 10 pages + 3 pages of supplementary. Conference style pape