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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 study aims to investigate profoundly the protection of oil painting from deterioration using molybdenum trisulphide quantum dots (MoS3 QDs) against microbe, dirt accumulation and ultraviolet (UV) degradation.

The protection of painting against different deterioration factors necessitates the sustainable methods and advanced techniques. Scanning electron microscopy and transmission electron microscopy have been used to investigate the morphological structure of the painting and MoS3 QDs, respectively, and optical microscopy was used to examine antibacterial activity of MoS3 QDs towards different types of bacteria. To investigate the protection of painting against deterioration, the Fourier transform IR spectroscopy (FTIR) was used to investigate the paintings left in open air for a year. Chemical composition and crystal structure of MoS3 QDs have been studied using X-ray diffraction and X-ray photoelectron spectroscopy analysis, respectively.

The addition of MoS3 nanoparticles into painted coatings enhances the durability of linseed oil-based paintings toward UV ageing regarding the change in colour which confirmed by FTIR analysis. The protection of oil painting opposed to various deterioration factors was developed by involving of MoS3 QDs in the coating of the painting. Antibacterial effect of MoS3 QDs was tested against different types of bacteria such as Pseudomonas aeruginosa confirming that the MoS3 QDs involved in the coatings of oil paintings produces a high protection layer for the paintings against several microbial attacks. In addition, coatings containing MoS3 QDs reduce the accumulation of dirt on oil paintings when subjected to open air for a year.

The novel MoS3 QDs was used to form a protective and transparent coating layer for the oil painting to overcome the deterioration, displays the promising protection and can be applied for different oil paintings.

We articulate cycles of creative storying and data analysis and the wonder that motivated the project by detailing our reading, thinking and writing processes to contribute to the conceptual and practical literature on collaborative writing as method.

This paper documents a process of collaborative writing as method that produced a co-written fictional narrative that evokes questions about schooling.

Our process began with world-building for our fictional future, creating characters to serve as a lens for analysis. From our discussions of the data, we crafted a plotline to present our themes in coherent story. As we explored and shared our readings, interdisciplinarity shaped our project in unique ways.

Throughout, our collaboration and fiction-writing opened spaces for wonder, interdisciplinarity and joy that may inspire other researchers to engage in collaborative and/or creative writing processes.

Adding to the rich literature of arts-based research methods, we contribute our reflections on the pragmatics of incorporating reading, writing and thinking for collaboration and creative writing as qualitative research methods for document analysis.

The purpose of this study is to evaluate the effectiveness of nanocontainer solutions in removing deteriorated and aged polymers commonly used in coating and consolidating archaeological glass.

This study focused on characterizing glass surfaces coated with two commonly used polymeric materials in archaeological glass preservation. Furthermore, the study evaluates the elimination of these coatings from the surfaces by using innovative aqueous systems composed of micellar solutions (MS) and oil-in-water (O/W) Texapon-P microemulsions (TEX). Glass samples coated with selected polymers were subjected to thermal and photochemical aging to simulate natural degradation conditions. This study aimed to evaluate the effectiveness of nanocontainer aqueous systems compared to acetone (Ac), a conventional solvent commonly used for removal procedures. The characterization procedures involved transmission electron microscopy, USB digital microscopy, scanning electron microscopy, color alteration and gravimetric measurement.

The findings indicate that the effectiveness of novel techniques using aqueous nanocontainer systems is quite promising when considering a “green approach” to preserving cultural heritage. Microscopic examination demonstrated the efficacy of MS in effectively removing acrylic and vinyl polymers from the glass surface. Furthermore, TEX proved effective in removing polyvinyl acetate (PVA) over Paraloid B72 (B-72). In addition, the measurement of color alteration values revealed a decrease after using MS compared to the standard sample before applying the polymers, with values of ΔE = 1.48 and 1.82 for B-72 polymer and PVA, respectively.

This research provides nanocontainer solutions for removing aged polymers from the glass surface. This makes the current study a promising step for treating archaeological glass.

The purpose of this paper is to address the challenging issue of developing a quantitative approach for the representation of cultural significance data in heritage information systems (HIS). The authors propose to provide experts in the field with a dedicated framework to structure and integrate targeted data about historical objects' significance in such environments.

This research seeks the identification of key indicators which allow to better inform decision-makers about cultural significance. Identified concepts are formalized in a data structure through conceptual data modeling, taking advantage on unified modeling language (HIS). The design science research (DSR) method is implemented to facilitate the development of the data model.

This paper proposes a practical solution for the formalization of data related to the significance of objects in HIS. The authors end up with a data model which enables multiple knowledge representations through data analysis and information retrieval.

The framework proposed in this article supports a more sustainable vision of heritage preservation as the framework enhances the involvement of all stakeholders in the conservation and management of historical sites. The data model supports explicit communications of the significance of historical objects and strengthens the synergy between the stakeholders involved in different phases of the conservation process.

This paper aims to investigate the ability of traditional biopolymers, such as funori or the nanoscale form of cellulose nanocrystals, to consolidate fragile paper and preserve it for as long as possible.

Degraded papers dating back two centuries were separated into paper samples for consolidation processes. Funori – a marine spleen – was used as a traditional consolidation material and a mixture with ZnO NPs compared with modern materials, such as cellulose nanocrystals. The samples were aged for 25 years, examinations and analyses were performed using scanning electron microscopy and color change was assessed using the CIELAB system, X-ray diffraction and Fourier-transform infrared spectroscopy.

According to the results, using traditional materials to consolidate damage, such as funori, after aging resulted in glossiness on the surface, a color change and increased water content and oxidation. Furthermore, samples treated with a mixture of ZnO NPs and funori revealed that the mixture improved the sample properties and increased the degree of crystallization. Cellulose nanocrystals improved the surface, filled gaps, formed bridges between the fibers and acted as a protector from aging effects.

This paper highlights the ability of nanomaterials to enhance the properties of materials as additives and treat the paper manuscripts from weaknesses.

This study aims to offer an effective nanocomposite for potential use to consolidate and protect deteriorated archaeological pottery.

Three nanocomposites were used in the experimental study. This study used nano Primal AC33, silicon dioxide (SiO₂) and montmorillonite (MMT) nanoparticles to protect and consolidate pottery specimens. Pottery specimens were made at 800°C for this investigation. Consolidation materials were applied with a brush. The properties of the treated pottery specimens were assessed using several methods such as digital and scanning electron microscopes, static water contact angle, color alteration, physical properties and compressive strength.

Microscopic examination indicated the ability of the nano Primal AC33/MMT nanocomposites to cover the outer surface well and bind the inner granules. Concerning specimens with code F treated with nano Primal AC33 5%/MMT 3% nanocomposites, it achieved an increase in contact angle (120°), density (1.23 g/cm³) and compressive strength (561 kg/cm²), as well as a decrease in color change (ΔE = 2.62), water absorption (4.45%) and porosity (5.46%). The novelty of the results is due to the characteristics of nano Primal AC33 5%/MMT 3% nanocomposites used in the current study.

This study describes the significant results of the analytical methods used for evaluating consolidation materials used in this study. The findings offer useful information for the protection of archaeological pottery. The investigation indicated that nano Primal AC33 5%/MMT 3% nanocomposites gave the best results. Therefore, it is recommended to use this nanocomposite to consolidate archaeological pottery. As a result, the current work provides a promising first step in conserving archaeological pottery for future studies.

This study aims to evaluate the efficiency of using some polymers at different concentrations in the consolidation of vegetable-tanned leather artifacts.

New vegetable-tanned leather samples were prepared. The consolidants used were polyacrylamide (PAM) and polymethyl methacrylate/hydroxyethyl methacrylate (MMA-HEMA). Accelerated heat aging was applied to the untreated and treated samples. Analytical techniques used were Fourier transform infrared spectroscopy (FTIR), digital microscope, scanning electron microscope (SEM), change of color and mechanical properties.

The characteristic FTIR bands showed the effect of accelerated heat aging on the molecular structure of the studied samples, but treated and aged treated samples used were better than aged untreated samples. Microscopic investigations (digital and SEM), and mechanical properties proved that 2% was the best concentration for polymers used. The change in the total color difference of the treated and aged treated samples was limited.

This study presents the important results obtained from PAM and poly(MMA-HEMA) used for the consolidation of vegetable-tanned leather artifacts. The best results of the studied polymers can be applied directly to protect historical vegetable-tanned leathers.

Paper aims to evaluate the efficiency of traditional chitosan, nano chitosan, and chitosan nanocomposites for consolidating aged papyrus samples. Cellulose-based materials, such as papyrus sheets and paper, which are the most common types of writing supports for works of art in many museums and archive. They are subjected to different types of deterioration factors that may lead to many conservation problems. Consolidation treatment is one of the most common conservation treatments, which should have perform after much testing to select the appropriate consolidants.

This research paper aims to evaluate the resistance of traditional chitosan, nanochitosan and chitosan/zinc oxide nanocomposite as an eco-friendly papyrus strengthening. Untreated and treated papyrus was thermally aged and characterized via scanning electron microscope (SEM) and Fourier transform infrared spectroscopy (FTIR). Antimicrobial activity of the papyrus specimens was also determined against four tested pathogenic bacteria by disc diffusion method: MRSA, Staphylococcus aureus, E. coli and P. aeruginosa.

The results revealed that chitosan nanocomposite showed a remarkable enhancement of papyrus tensile properties and presence of ZnO prevents the effects of biodeterioration.

Zinc oxide nanoparticles enhance the optical properties and increase the chemical reactions between the consolidating material and the treated papyrus.

This paper aims to propose a research agenda towards a holistic, grounded and flexible approach to cultural heritage that can address social challenges and transformations in the context of place. It critiques the dominant/hegemonic cultural heritage narratives, deriving from juridification and calls for a grounded approach in the way cultural heritage is framed and experienced.

The paper is conceptual, focusing on the need to open a line of enquiry into the relationship between legal texts, cultural heritage narratives and social challenges and transformations. It follows the letter of the international conventions on cultural heritage against the worked example of the medieval town of Rhodes in Greece.

The paper sets the relevant research priorities for the investigation of the effective relationship between cultural heritage and social challenges in the context of place, and further stretches the need to evaluate the role of legal and regulatory texts to that effect.

The paper identifies new priorities for thinking about the effects of juridification/the law, cultural heritage and social challenges/transformations in a place-specific context. It seeks to open new avenues of scientific explorations and new interdisciplinary dialogues between a variety of disciplines that are relevant to the way a place engages and addresses social challenges and transformations.