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Archaeological predictive maps can be valuable tools for planning officers, spatial developers and archaeologists. In 2012, the Dutch Ministry of Education, Culture and Science asked the St Eustatius Center for Archaeological Research (SECAR) to create archaeological predictive maps for St Eustatius and Saba. The maps were supposed to contribute to more effective management and protection of archaeological sites. The maps were published in 2013 and 2015. The purpose of this paper is to address the question whether the subsequent use of these maps has indeed improved site management and protection.

The evaluation proceeded by identifying the users and applications of the maps through questionnaires and an internet search engine, along with follow-up interviews with heritage professionals on both islands.

The use of the predictive maps has not yet contributed to improved management and protection of archaeological sites on St Eustatius and Saba. Suggestions have been made to ameliorate this situation and to ensure the preservation of archaeological heritage for future generations.

The creation of archaeological predictive maps can be an important step towards improving management and protection of archaeological heritage, education and heritage awareness. This is important for St Eustatius and Saba, islands that are characterised by vulnerable archaeological heritage, rapid spatial development and a lack of efficient protection measures. This paper points to the importance of follow-up efforts to ensure effective site management and protection.

This study was conducted with the main purpose of recording primary data related to environmental factors, which has become the main criteria in the selection of the Sungai Batu Archaeological Complex (SBAC) as the center of the iron smelting industry and trade in ancient Kedah.

To fulfill this purpose, field studies involving drone photogrammetry mapping, augering, core drilling and geophysical mapping methods were carried out.

The results obtained through the application of the method have shown that SBAC has a good environment, which has a wide and deep river flow, the existence of Mount Jerai and the abundance of iron ores, mangrove Merbok and clay.

Resources did not allow for environment studies of the by-products tourism sites as part of the current study.

The study also included a survey and mapping to obtain potential primary data around SBAC in the process of developing it as the center of the world iron industry.

One finding is that attention to heritage policy and protection must be ongoing at all levels of government and the local community to ensure that the survey and mapping data carried out can be developed as a sustainable heritage tourism product.

This study reveals primary data related to the suitability of paleoenvironment in the SBAC development process as a world iron smelting industry area.

The ancient monuments of Taormina represent one of the richest archaeological heritage of Sicily. Some of them, such as the Hellenistic-Roman Theatre, the odeum, the so-called Naumachia, are annually frequented by thousands of tourists, while others remain practically unknown to the general public. The urban fabric of the Hellenistic-Roman period of which these monuments were part was profoundly modified by the transformations that the town has known, having grown on itself almost without interruption until today. For these reasons, as in many other cases of cities with continuity of occupation, ancient monuments are perceived by the visitor as decontextualized relics, immersed in a very suggestive landscape, but very different from the original one. Usually the didactic apparatus available to visitors is not able to give knowledge of the original relations between monument and city. Decades of topographical and archaeological research have allowed us to identify the ancient urban layout of Taormina, and it is therefore possible today to take up the challenge of offering the public a deeper and more complete knowledge of its archaeological monuments in their ancient context. In fact, thanks to modern technologies, it is now possible to share the results of archaeological research with a wider public and to virtually recreate the ancient urban landscape. This contribution intends to present the solutions made available by these technologies to define a new strategy to enhance the city's archaeological heritage and to offer the public a truly immersive experience in its history.

The purpose of this paper is to demonstrate the importance of exploiting a geographic information system (GIS)-based data management, designed and implemented for an important monumental site. In particular, data collected during the years have been used to create a storytelling experience to disseminate the tangible and intangible heritage of Chan Chan (Peru), the wider site in mud bricks of Latin America.

The paper discusses the steps that have been performed to use the data stored in a GIS, arguing over the importance of sharing the knowledge through web-based tools, and in particular by the implementation of a storytelling. In this context, the data were structured in interoperable forms in order to preserve the universal value of the archaeological site. The exploitation in an all-in-one solution of the archival research, field surveys and planning represents a step forward for let known ancient testimonies to the whole mankind.

The GIS-based inventories represent the backbone for an affordable management of heritage resources. The novelty of the proposed approach lies on the creation of an integrated, accessible and updatable data system sharable on web.

The GIS of Chan Chan is an example of documentation of a wide archaeological area (14 km²) with complex and heterogeneous data. The developed web tool makes use of these data which can be queried even by non-expert users. The pipeline of this paper can act as useful guidelines to practitioners and researchers who want to disseminate cultural information.

The purpose of this paper is to nuance the perception about professional documentation (a.k.a. “grey literature”), assuming perception of documentation being a cultural aspect of accessibility.

The study explores variations within the archaeological report genre through a bibliometric analysis of source use. Source characteristics are explored as well as correlations between report authors and source originators. Statistical frequency distribution is complemented by a correspondence analysis and a k-means cluster analysis to explore patterns. The patterns are interpreted as “frames of references” and related to circumstances for archaeological work. The study also discusses source representations.

The source use patterns reveal a latent variation, not visible in the general analysis: a professional/academic frame of reference (mainly among authors affiliated with incorporated businesses and sole proprietorships) and an administrative frame of reference (mainly among authors affiliated with government agencies, foundations, and member associations) emerge.

The study focuses on Swedish field evaluation reports. Future research could test the results in relation to other types of reports and go beyond the document perspective to explore source use in documentation practices.

The results on variations in frames of references among report writers have implication for report readers and user. The results should also be considered in archaeology management and policy-making. On the level of source representation the results call for clarifications of vague representations and possibly omitted sources.

This study contextualizes archaeological information use and focuses on variations in professional archaeology which has received little previous research attention. The bibliometric approach complements previous qualitative studies of archaeological information.

This study aims to compare the local climate characteristics of Angkor Wat, Borobudur and Prambanan parks and determine effective strategies for mitigating thermal conditions that could suit Borobudur and Angkor Wat.

The study employed local climate zone (LCZ) indicators and ten-year historical climate data to identify similarities and differences in local climate characteristics. Satellite imagery processing was used to create maps of LCZ indicators. Meanwhile, microclimate models were used to analyze sky view factors and wind permeability.

The study found that the three tropical large-scale archaeological parks have low albedo, a medium vegetation index and high impervious surface index. However, various morphological characteristics, aerodynamic properties and differences in temple stone area and altitude enlarge the air temperature range.

Based on the similarities and differences in local climate, the study formulated mitigation strategies to preserve the sustainability of ancient temples and reduce visitors' heat stress.

The local climate characterization of tropical archaeological parks adds to the number of LCZs. Knowledge of the local climate characteristics of tropical archaeological parks can be the basis for improving thermal conditions.

The purpose of this paper is to present a concurrent implementation of sustainable inventory for the UNESCO World Heritage sites of Bat, Al-Khutm and Al-Ayn in the interior of Oman.

A digital heritage inventory (DHI) was developed through an action research to realize demands of the local agent and to co-design the solution. The Ministry of Heritage and Culture of Oman, the local agent, demanded to have archaeological information of the sites shared with foreign expeditions, which had worked at the sites for decades, for efficient heritage management, scientific research, outreach, and education. To this end, the Bat Digital Heritage Inventory (BatDHI) was implemented by a combination of network-access-ready database application, open source geographical information systems, and a web-based map service to incorporate and visualize previous works, which were concurrently cross-checked and updated by ground-truth surveys.

The online inventory made it possible to update information during archaeological fieldwork in real time and accelerated the decision-making process in heritage management by prompt data updates and visualization.

The DHI is extendable for other sites or regions. It should also be considered to install Arches, an open-source suite of digital heritage inventories.

The BatDHI was implemented through the action research mentioned in the design/methodology/approach section and yielded the implications mentioned in the findings section.

This paper is a challenging application of transdisciplinary approach to the sustainable heritage management, in which researchers and societal stakeholders collaborate for co-design of research agendas, co-production of knowledge, and co-dissemination of outcomes.

This paper provides practical advice for archaeologists and heritage specialists wishing to use ML approaches to identify archaeological features in high-resolution satellite imagery (or other remotely sensed data sources). We seek to balance the disproportionately optimistic literature related to the application of ML to archaeological prospection through a discussion of limitations, challenges and other difficulties. We further seek to raise awareness among researchers of the time, effort, expertise and resources necessary to implement ML successfully, so that they can make an informed choice between ML and manual inspection approaches.

Automated object detection has been the holy grail of archaeological remote sensing for the last two decades. Machine learning (ML) models have proven able to detect uniform features across a consistent background, but more variegated imagery remains a challenge. We set out to detect burial mounds in satellite imagery from a diverse landscape in Central Bulgaria using a pre-trained Convolutional Neural Network (CNN) plus additional but low-touch training to improve performance. Training was accomplished using MOUND/NOT MOUND cutouts, and the model assessed arbitrary tiles of the same size from the image. Results were assessed using field data.

Validation of results against field data showed that self-reported success rates were misleadingly high, and that the model was misidentifying most features. Setting an identification threshold at 60% probability, and noting that we used an approach where the CNN assessed tiles of a fixed size, tile-based false negative rates were 95–96%, false positive rates were 87–95% of tagged tiles, while true positives were only 5–13%. Counterintuitively, the model provided with training data selected for highly visible mounds (rather than all mounds) performed worse. Development of the model, meanwhile, required approximately 135 person-hours of work.

Our attempt to deploy a pre-trained CNN demonstrates the limitations of this approach when it is used to detect varied features of different sizes within a heterogeneous landscape that contains confounding natural and modern features, such as roads, forests and field boundaries. The model has detected incidental features rather than the mounds themselves, making external validation with field data an essential part of CNN workflows. Correcting the model would require refining the training data as well as adopting different approaches to model choice and execution, raising the computational requirements beyond the level of most cultural heritage practitioners.

Improving the pre-trained model’s performance would require considerable time and resources, on top of the time already invested. The degree of manual intervention required – particularly around the subsetting and annotation of training data – is so significant that it raises the question of whether it would be more efficient to identify all of the mounds manually, either through brute-force inspection by experts or by crowdsourcing the analysis to trained – or even untrained – volunteers. Researchers and heritage specialists seeking efficient methods for extracting features from remotely sensed data should weigh the costs and benefits of ML versus manual approaches carefully.

Our literature review indicates that use of artificial intelligence (AI) and ML approaches to archaeological prospection have grown exponentially in the past decade, approaching adoption levels associated with “crossing the chasm” from innovators and early adopters to the majority of researchers. The literature itself, however, is overwhelmingly positive, reflecting some combination of publication bias and a rhetoric of unconditional success. This paper presents the failure of a good-faith attempt to utilise these approaches as a counterbalance and cautionary tale to potential adopters of the technology. Early-majority adopters may find ML difficult to implement effectively in real-life scenarios.

Unlike many high-profile reports from well-funded projects, our paper represents a serious but modestly resourced attempt to apply an ML approach to archaeological remote sensing, using techniques like transfer learning that are promoted as solutions to time and cost problems associated with, e.g. annotating and manipulating training data. While the majority of articles uncritically promote ML, or only discuss how challenges were overcome, our paper investigates how – despite reasonable self-reported scores – the model failed to locate the target features when compared to field data. We also present time, expertise and resourcing requirements, a rarity in ML-for-archaeology publications.

This paper sets out to discuss the use of information extraction (IE), a natural language‐processing (NLP) technique to assist “rich” semantic indexing of diverse archaeological text resources. The focus of the research is to direct a semantic‐aware “rich” indexing of diverse natural language resources with properties capable of satisfying information retrieval from online publications and datasets associated with the Semantic Technologies for Archaeological Resources (STAR) project.

The paper proposes use of the English Heritage extension (CRM‐EH) of the standard core ontology in cultural heritage, CIDOC CRM, and exploitation of domain thesauri resources for driving and enhancing an Ontology‐Oriented Information Extraction process. The process of semantic indexing is based on a rule‐based Information Extraction technique, which is facilitated by the General Architecture of Text Engineering (GATE) toolkit and expressed by Java Annotation Pattern Engine (JAPE) rules.

Initial results suggest that the combination of information extraction with knowledge resources and standard conceptual models is capable of supporting semantic‐aware term indexing. Additional efforts are required for further exploitation of the technique and adoption of formal evaluation methods for assessing the performance of the method in measurable terms.

The value of the paper lies in the semantic indexing of 535 unpublished online documents often referred to as “Grey Literature”, from the Archaeological Data Service OASIS corpus (Online AccesS to the Index of archaeological investigationS), with respect to the CRM ontological concepts E49.Time Appellation and P19.Physical Object.

By reconsidering the concept of the historic environment, the aim of this study is to better understand how heritage is expressed by examining the networks within which the cultural performances of the historic environment take place. The goal is to move beyond a purely material expression and seek the expansion of the cultural dimension of the historic environment.

Conceptually, the historic environment is considered a valuable resource for heritage expression and exploration. The databases and records that house historic environment data are venerated and frequented entities for archeologists, but arguably less so for non-specialist users. In inventorying the historic environment, databases fulfill a major role in the planning process and asset management that is often considered to be more than just perfunctory. This paper approaches historic environment records (HERs) from an actor network perspective, particularizing the social foundation and relationships within the networks governing the historic environment and the environment's associated records.

The paper concludes that the performance of HERs from an actor-network perspective is a hegemonic process that is biased toward the supply and input to and from professional users. Furthermore, the paper provides a schematic for how many of the flaws in heritage transmission have come about.

The relevance here is largely belied by the fact that HERs as both public digital resources and as heritage networks were awaiting to be addressed in depth from a theoretical point of view.