In the last decades, the interest in the development of protective coatings for movable and immovable Cultural Heritage (CH) assets has decidedly increased. This has been mainly prompted by the raising consciousness on preservation requirements for cultural artefacts and monuments, which has consequently determined the development of new protective products. From acrylic resins used at the end of the last century to the up-to-date biomaterials and nanoparticles employed nowadays, the research has made a giant step forward. This article reviews the progresses, the technical challenges, and the most recent advances in protective coatings for archaeological metal, glass, and stone artefacts. It aims at offering a comprehensive and critical overview of the progressions in conservation science and displaying how research has optimized polymers in order to solve deterioration problems. Attention is given to recently developed materials, hybrid coatings, and corrosion inhibitors. This work seeks to provide a reference point for future research and to offer a wide-ranging introduction on the newly available material technologies to restorers and conservators.

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 of landscape patterns on Remote Sensing imagery has seen virtually little or no development in the archaeological domain, notwithstanding the fact that large portion of cultural landscapes worldwide are characterized by land engineering applications. The current extraordinary availability of remotely sensed images makes it now urgent to envision and develop automatic methods that can simplify their inspection and the extraction of relevant information from them, as the quantity of information is no longer manageable by traditional “human” visual interpretation. This paper expands on the development of automatic methods for the detection of target landscape features—represented by field system patterns—in very high spatial resolution images, within the framework of an archaeological project focused on the landscape engineering embedded in Roman cadasters. The targets of interest consist of a variety of similarly oriented objects of diverse nature (such as roads, drainage channels, etc.) concurring to demark the current landscape organization, which reflects the one imposed by Romans over two millennia ago. The proposed workflow exploits the textural and shape properties of real-world elements forming the field patterns using multiscale analysis of dominant oriented response filters. Trials showed that this approach provides accurate localization of target linear objects and alignments signaled by a wide range of physical entities with very different characteristics.

Machine Learning-based workflows are being progressively used for the automatic detection of archaeological objects (intended as below-surface sites) in remote sensing data. Despite promising results in the detection phase, there is still a lack of a standard set of measures to evaluate the performance of object detection methods, since buried archaeological sites often have distinctive shapes that set them aside from other types of objects included in mainstream remote sensing datasets (e.g., Dataset of Object deTection in Aerial images, DOTA). Additionally, archaeological research relies heavily on geospatial information when validating the output of an object detection procedure, a type of information that is not normally considered in regular machine learning validation pipelines. This paper tackles these shortcomings by introducing two novel automatic evaluation measures, namely ‘centroid-based’ and ‘pixel-based’, designed to encode the salient aspects of the archaeologists’ thinking process. To test their usability, an experiment with different object detection deep neural networks was conducted on a LiDAR dataset. The experimental results show that these two automatic measures closely resemble the semi-automatic one currently used by archaeologists and therefore can be adopted as fully automatic evaluation measures in archaeological remote sensing detection. Adoption will facilitate cross-study comparisons and close collaboration between machine learning and archaeological researchers, which in turn will encourage the development of novel human-centred archaeological object detection tools.