This study presents a multi-resolution and multi-temporal remote sensing approach to assess human-induced changes in cultural landscapes, with a focus on the archaeological site of Amrit (Syria) within the MapDam project. By integrating satellite archives (KH, Landsat series, NASADEM) with ancillary geospatial data (OpenStreetMap) and advanced analytical methods, four decades (1984–2024) of land-use/land-cover (LULC) change and shoreline dynamics were reconstructed. Machine learning classification (Random Forest) achieved high accuracy (Test Accuracy = 0.94; Kappa = 0.89), enabling robust LULC mapping, while predictive modelling of urban expansion, calibrated through a Gradient Boosting Machine, attained a Figure of Merit of 0.157, confirming strong predictive reliability. The results reveal path-dependent urban growth concentrated on low-slope terrains (≤5°) and consistent with proximity to infrastructure, alongside significant shoreline regression after 1974. A Business-as-Usual projection for 2024–2034 estimates 8.676 ha of new anthropisation, predominantly along accessible plains and peri-urban fringes. Beyond quantitative outcomes, this study demonstrates the replicability and scalability of open-source, data-driven workflows using Google Earth Engine and Python 3.14, making them applicable to other high-risk heritage contexts. This transparent methodology is particularly critical in conflict zones or in regions where cultural assets are neglected due to economic constraints, political agendas, or governance limitations, offering a powerful tool to document and safeguard endangered archaeological landscapes.

This study shows and discusses an innovative approach devised for archaeological feature detection using unmanned aerial system (UAS) LiDAR and an open-source probabilistic machine learning framework. The methodology employs a Random Forest classification algorithm within CloudCompare’s 3DMASC plugin to analyse dense LiDAR point clouds. The main steps include classifier training, hyperparameter adjustment and point cloud segmentation to produce digital terrain models (DTM), digital feature models (DFM) and digital surface models (DSM). Experimenting different parameters led to the determination of the best set to be employed for the training model. Subsequent data enhancement with the Relief Visualisation Toolbox (RVT) refines the visibility of archaeological features, particularly within complex and heavily vegetated terrain. The use case selected to validate this approach is the site of Kastrí-Pandosia in Epirus (Greece), which is particularly suitable for LiDAR analysis by UAS. This approach significantly improves archaeological detection and interpretation, revealing previously inaccessible or obscured microtopographic and structural features. The results highlight the site’s defensive walls, terracing and potential anthropogenic routes, underlining the methodology’s effectiveness in detecting archaeological landscapes at multiple levels. This study emphasises the utility of accessible and open-source solutions for the identification of archaeological features, promoting cost-effective methods to improve the documentation of sites in remote or difficult locations.

Street art murals are increasingly recognized as valuable contemporary artworks, often attaining significant artistic, historical, and social importance. As these murals become integral parts of cultural heritage, finding efficient strategies for their conservation is crucial. However, their typical large surface areas, heterogeneous materials, and high variability in exposure to environmental and pollution factors pose significant challenges in establishing appropriate analytical strategies to obtain the necessary information. This study proposes a multiscale and multitechnique noninvasive approach to investigate and monitor street art murals in situ. By combining portable point techniques-such as external reflection Fourier transform infrared spectroscopy, Raman spectroscopy, visible, near infrared, and short-wave infrared reflectance spectroscopy, and X-ray fluorescence spectroscopy-with visible and near infrared hyperspectral imaging, the mural's composition across a square meter surface could be analyzed. Additionally, multispectral imaging mounted on a drone provided a global reconstruction and characterization of the overall mural. This method was complemented by microdestructive laboratory analyses of selected samples, using pyrolysis gas chromatography-mass spectrometry and liquid chromatography coupled with diode array detector and tandem mass spectrometry, to further investigate selected samples and support noninvasive results. The approach was applied to the iconic mural "Musica Popolare" (2017) by Orticanoodles in Milan, Italy, revealing detailed information about its pigments, binders, fillers, and degradation. The findings demonstrate the potential of this integrated methodology for the effective material identification, conservation assessment, and short-and long-term monitoring of urban heritage.

In this study a methodological framework for enhancing the detection and interpretation of archaeological features through near-surface geophysical surveys, in particular Ground Penetrating Radar (GPR) and magnetic gradiometry (MAG) is presented. Consequently a combined approach based on spatial analysis techniques and Artificial Intelligence, specifically Self-Organizing Maps (SOM), is devised to support automatic feature enhancement and recognition. This method has been experienced using GPR and gradiometric surveys, performed in a use case inside the archaeological area of Grumentum (Southern Italy). The results highlight the effectiveness of this approach in improving the readability of complex and heterogeneous geophysical datasets and increase the reliability of archaeological interpretations and in identifying subsurface remains and facilitating their interpretation. It is expected that the approach herein proposed can be promptly generalized and applied to other application fields.

The deterioration of stone materials due to atmospheric factors is a growing global concern, affecting the integrity and preservation of numerous UNESCO World Heritage Sites around the world. This study provides an estimate of the long-term impact of the climate on the degradation of carbonate stone materials in the UNESCO site of Matera, in southern Italy. Focusing on Gravina calcarenite, a lithotype susceptible to weathering, the research integrates satellite-derived precipitation data from the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) with a dose-response model. The method involves the calibration of CHIRPS precipitation records against ground-based meteorological data, and the use of year-specific recession coefficients Ky dynamically computed as a function of atmospheric CO2 concentration and temperature. These coefficients were applied within a Lipfert-based equation to estimate annual surface recession from 1981 to 2040 (near future). The results reveal a continuous increase in surface degradation over time, with the cumulative material loss reaching approximately 0.75 mm by 2040. These findings underscore the relevance of climate-responsive models in estimating stone decay and provide a critical basis for adaptive conservation planning. Incorporating future climate projections into risk assessments is essential for the sustainable preservation of carbonate-based cultural heritage exposed to atmospheric and hydrological stressors.

This article presents a multiparametric and multiscale methodological approach devised during the COVID-19 pandemic event for the environmental monitoring of artworks housed in a museum. The pandemic event provided the opportunity to analyze the museum environment in the absence of visitors, thus enabling the evaluation of indoor conditions solely influenced by environmental, meteorological, and microclimatic factors. The experimental analysis was conducted in the Altemps Palace in Rome. Microclimatic variables, such as temperature, relative humidity, and air quality (PM10 and PM2.5) were monitored using a network of MeMS sensors, which were made interoperable through Long Range transmission technology. To establish a comparative reference, indoor were compared with outdoor measurements, along with ground measurements (provided by the Regional Prevention and Environment Agency, and weather forecasts based on the COSMO model. At the same time, analyses were conducted on the conservation status of the exhibited artworks. The analysis of the collected data revealed critical issues regarding the indoor microclimatic conditions, particularly related to the significant variability of the relative humidity. Additionally, numerous episodes exceeding the maximum threshold for PM10 and PM2.5 were logged, emphasizing pollution concerns. The research findings have practical implications for the conservation and protection of the artworks housed in the museum.

In recent years, street art has been recognised as a form of cultural heritage. However, its ephemeral nature, coupled with continuous exposure to environmental and anthropogenic factors such as vandalism, pollution, and urban development, poses significant challenges for its documentation, conservation, and long-term preservation. These challenges are further compounded by the often inaccessible or large-scale urban surfaces on which street art is created, requiring flexible, non-invasive, and high-resolution monitoring techniques. In response, this study presents a multi-modal approach that integrates hyperspectral imaging (VIS-NIR) and thermal imaging from unmanned aerial vehicles (UAVs), laser scanners and ground hyperspectral instrumentation to analyse four mural works in Turin and Milan. In a 4-hour flight, an extremely high spatial resolution hyperspectral and thermal dataset was acquired, covering over 850 m(2) of surface area. The images, calibrated and subsequently geometrically corrected by laser scanner, were then compared with ground data from high specific spectroscopic point analyses and hyperspectral imaging, classified with Spectral Angle Mapper (SAM). The results highlight the potential of using UAVs to study large wall surfaces, the speed of acquisition in complex contexts and the possibility of extending this technique, contributing to new strategies for the conservation and management of street art.

A recent archaeological campaign discovered a large settlement (i.e.: the Santa Gada site) dated to 6th-3rd centuries BCE. in the Pollino Geopark, southern Italy. The study area is located in a Middle Pleistocene tectonic basin of the axial zone of the southern Apennines, Italy, filled by a thick fluvio-lacustrine succession. The landscape is featured by different orders of SSO-dipping low-angle surfaces that are separated by rectilinear scarps of variable height. Strict relationships among fluvial landforms, active faulting, and settlement evolution have been investigated by the integration of geological, geomorphological, and geophysical data. The morphogenesis of landform elements, the role of major faults as seismogenic sources, and the relationships between landscape and settlement location were discussed in the context of the long-term landscape evolution of the study area. The settlement was built on the top of a fluvial terrace and is bounded northward by a morphological scarp. Archaeological data revealed a sudden abandonment of a wide residential complex at the end of the 3rd century BCE. Radiocarbon dating of a human skull involved in the collapse of a building confirms the archaeological age attribution, thus providing the first archaeoseismological evidence of a strong historical earthquake in the Pollino area. Our investigations suggest a fluvial origin of the morphological scarp of the study area and a possible role of important seismogenic sources of the Pollino area for the sudden abandonment of the site.

Monitoring of ancient buildings is an issue of great interest in view of a proper restoration. This paper describes the noninvasive monitoring of the Domus Nozze D'Argento in Pompeii. The Roman house, as occurred for many other buildings in Pompei, was buried in the ash from the 79 ad eruption of Mount Vesuvius. It was excavated in 1893, the year of the silver wedding anniversary of King Umberto and Queen Margherita of Savoy. This event gave the name to the Roman domus whose excavations uncovered a monumental architecture composed of an atrium and two gardens. The atrium is characterized by four tall Corinthian columns and an elegant exedra with fine decoration. Of the two gardens, one, the larger, includes a central pool and a triclinium, and the other one is composed of a bathhouse, open-air swimming pool and a living room embellished by a mosaic floor and wall paintings. Over time, the weathering and the presence of heavy and rigid concrete structures have caused some static problems to be addressed by restorations respectful of the mechanical behaviour of the original load-bearing framework. To collect data useful for structural diagnosis, the geophysical investigation was undertaken based on the use of ground penetrating radar (GPR). The results allowed to evidence the conservation state of the investigated walls and columns using the high frequency antenna (2 GHz) and the identification of buried pipes using the 600-MHz antenna data.

This study introduces a methodology for the improvement of the visibility of archaeological features using an open-source probabilistic machine learning framework applied to UAV LiDAR data from the Torre Castiglione site in Apulia, Italy. By leveraging a Random Forest classification algorithm embedded in an open-source software, the approach processes dense LiDAR point clouds to segment out vegetation from the ground and the structures. Key steps include training the classifier, generating digital terrain models, digital feature models, and digital surface models, and enhancing the visibility of archaeological features. This method has proven effective in improving the interpretation of archaeological sites, revealing previously hidden or difficult-to-access microtopographic and structural details, such as the defensive structures, terraces, and ancient paths of the Torre Castiglione site. The results underline this methodology’s ease of use in uncovering archaeological landscapes under a dense canopy. Moreover, the study emphasises the benefits of using open-source tools to enhance the documentation and analysis of remote or difficult archaeological sites.

Preventive and planned conservation (PPC) responds to many of the principles of the circular economy, first of all the extension of the life cycle, and can effectively contribute to the definition of new sustainable development actions of the heritage, territories, and com-munities. By substantiating itself in the planning of monitoring, maintenance and improve-ment activities of environmental conditions, through the energy and climate efficiency of cultural heritage and historic buildings, the PPC is able to solve numerous challenges from a sustainable perspective, not only of conservation, but also management and valorisation of the heritage, to the point of achieving the reuse of abandoned structures, reducing both the use of new land to be allocated to the built heritage and the use of economic resourc-es linked to restoration interventions not linked to strategic planning. This contribution presents in an integrated, interdisciplinary, and systemic way, some experiments of the Basilicata Heritage Smart Lab project on the conservation of rock sites and the activation of communities in the co-governance processes of a public building in the UNESCO site of the Sassi di Matera: two aspects that are only apparently distant, which must be connected to ensure correct implementation of the CPP

The study focuses on the integrated use of multiscale and multisensor remote sensing techniques and big data analysis for the identification of buried archaeological remains or areas of potential archaeological interest. Satellite multispectral data (at very high and high resolution), drone based visible, multispectral, and thermal imagery, and geophysical prospecting (gradiometer) were used. The ancient city of Metaponto was chosen as case study, as it was a very important city in the formative panorama of Italian Magna Graecia and it also is one of the most important and best preserved archaeological sites in southern Italy. The analysis of remote sensing data from different sensors, with different resolutions, and referable to different physical parameters, allowed to deepen archaeological knowledge on a landscape scale, as well as on a site scale, going from the analysis of traces of the ancient landscape (e.g. palaeo-channels, canalisation system, main roads), to the discovery of small features (e.g. secondary roads, houses, facilities).

The constructive reading of the historical architecture through the study of the published and unpublished sources preserved in the archives, can represent a unique and precious cognitive tool to acquire all fundamental information that constitutes the basis of an aware restoration intervention.Frequently, in case of ancient buildings, reconstruct the complete history of the building is not possible, as the sources are difficult to find, not published or kept in different territorial institutes that are not always easily accessible. The aim of this work is to set up an HBIM (Heritage Building Information Model) system to facilitate the planning of diagnostic and restoration activities by bringing all archive information into a unique digital reference platform, accompanied by three-dimensional models that can be consulted, examined and updated.The creation of the HBIM digital tool for consulting the architectural artefact and related information was only the last phase of this work, which began with: (i) Acquisition of archive sources in order to reconstruct the history of the restoration and renovation work on the monument; (ii) Acquisition of information about the diagnostic analyses and monitoring previously carried out on the monument; (iii) acquisition of data useful for the creation of a digital twin.

The aim of this work is to set up an HBIM (Heritage Building Information Model) system to facilitate the planning of diagnostic and restoration activities by bringing all archive information into a digital platform, accompanied by three-dimensional models that can be consulted and examined.Frequently, in case of ancient buildings, reconstruct the complete history of the property is not possible, due to a lack of accessible sources.The creation of the HBIM digital tool for consulting the architectural artefact and related information was only the last phase of this work, which began with: (i) Acquisition of archive sources in order to reconstruct the history of the restoration and renovation work on the monument; (ii) Acquisition of information about the diagnostic analyses previously carried out on the monument; (iii) acquisition of data useful for the creation of a digital twin.

The present work aims to show the multisensor close-range remote sensing (RS) activities carried out in the municipality of Ascoli Satriano (FG; Apulia).The site is of great significance as it has been continuously occupied from the 8 th century B.C. to the present day.The site is famous for the discovery of marble griffins dated to the 4 th century B.C., probably inside a princely tomb.The study was conducted using a multispectral drone and a ground magnetometer.The results of the RS analysis showed a complex scenario, with features of buried remains related to roads, paleochannel, and, above all, circular structures of considerable size.The latter, as confirmed by the targeted excavations conducted following the RS acquisitions, are referable to monumental tumuli.The integration of several sensors has made it possible to overcome the limits of single instrument observation and to provide useful data for practical preventive archaeological work.

This study aimed to evaluate the impact of using an AI model, specifically ChatGPT-3.5, in remote sensing (RS) applied to archaeological research. It assessed the model’s abilities in several aspects, in accordance with a multi-level analysis of its usefulness: providing answers to both general and specific questions related to archaeological research; identifying and referencing the sources of information it uses; recommending appropriate tools based on the user’s desired outcome; assisting users in performing basic functions and processes in RS for archaeology (RSA); assisting users in carrying out complex processes for advanced RSA; and integrating with the tools and libraries commonly used in RSA. ChatGPT-3.5 was selected due to its availability as a free resource. The research also aimed to analyse the user’s prior skills, competencies, and language proficiency required to effectively utilise the model for achieving their research goals. Additionally, the study involved generating JavaScript code for interacting with the free Google Earth Engine tool as part of its research objectives. Use of these free tools, it was possible to demonstrate the impact that ChatGPT-3.5 can have when embedded in an archaeological RS flowchart on different levels. In particular, it was shown to be useful both for the theoretical part and for the generation of simple and complex processes and elaborations.