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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.

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.

Despite the recognized effectiveness of LiDAR in penetrating forest canopies, its capability for archaeological prospection can be strongly limited in areas covered by dense vegetation for the detection of subtle remains scattered over morphologically complex areas. In these cases, an important contribution to improve the identification of topographic variations of archaeological interest is provided by LiDAR-derived models (LDMs) based on relief visualization techniques. In this paper, diverse LDMs were applied to the medieval site of Torre Cisterna to the north of Melfi (Southern Italy), selected for this study because it is located on a hilly area with complex topography and thick vegetation cover. These conditions are common in several places of the Apennines in Southern Italy and prevented investigations during the 20th century. Diverse LDMs were used to obtain maximum information and to compare the performance of both subjective (through visual inspections) and objective (through their automatic classification) methods. To improve the discrimination/extraction capability of archaeological micro-relief, noise filtering was applied to Digital Terrain Model (DTM) before obtaining the LDMs. The automatic procedure allowed us to extract the most significant and typical features of a fortified settlement, such as the city walls and a tower castle. Other small, subtle features attributable to possible buried buildings of a habitation area have been identified by visual inspection of LDMs. Field surveys and in-situ inspections were carried out to verify the archaeological points of interest, microtopographical features, and landforms observed from the DTM-derived models, most of them automatically extracted. As a whole, the investigations allowed (i) the rediscovery of a fortified settlement from the 11th century and (ii) the detection of an unknown urban area abandoned in the Middle Ages.

This paper deals with a UAV LiDAR methodological approach for the identification and extraction of archaeological features under canopy in hilly Mediterranean environments, characterized by complex topography and strong erosion. The presence of trees and undergrowth makes the reconnaissance of archaeological features and remains very difficult, while the erosion, increased by slope, tends to adversely affect the microtopographical features of potential archaeological interest, thus making them hardly identifiable. For the purpose of our investigations, a UAV LiDAR survey has been carried out at Perticara (located in Basilicata southern Italy), an abandoned medieval village located in a geologically fragile area, characterized by complex topography, strong erosion, and a dense forest cover. All of these characteristics pose serious challenge issues and make this site particularly significant and attractive for the setting and testing of an optimal LiDAR-based approach to analyze hilly forested regions searching for subtle archaeological features. The LiDAR based investigations were based on three steps: (i) field data acquisition and data pre-processing, (ii) data post-processing, and (iii) semi-automatic feature extraction method based on machine learning and local statistics. The results obtained from the LiDAR based analyses (successfully confirmed by the field survey) made it possible to identify the lost medieval village that represents an emblematic case of settlement abandoned during the crisis of the late Middle Ages that affected most regions in southern Italy.

The construction history of a site is partially preserved underground and can be revealed through archaeological investigations, including excavations, integrated with earth observation (EO) methods and technologies that make it possible to overcome some operational limits regarding the areal dimensions and the investigation depths along with the invasiveness of the excavations themselves. An integrated approach based on EO and archaeological records has been applied to improve the knowledge of Machu Picchu. The attention has been focused on the first construction phase of Machu Picchu, and for this reason the investigations were directed to the imaging and characterization of the subsoil of the Plaza principal, considered the core of the whole archaeological area. Archaeological records and multiscale remote sensing (including satellite, UAS, and geophysical surveys) enabled the identification and characterization of the first construction phase of the site, including the preparation phases before building Machu Picchu. The interpretative hypothesis on the constructive history of Machu Picchu started from the identification and use of the quarry, followed by the planification and set of the drainage systems and by the next steps based on diverse reshaping phases of what would be the central plaza.

One of the major issues in buried archeological sites especially if characterized by intense human activity, complex structures, and several constructive phases, is: to what depth conduct the excavation? The answer depends on a number of factors, among these one of the most important is the a priori and reliable knowledge of what the subsoil can preserve. To this end, geophysics (if used in strong synergy with archaeological research) can help in the planning of time, depth, and modes of excavation also when the physical characteristics of the remains and their matrix are not ideal for archaeo-geophysical applications. This is the case of a great part of the archaeological sites in Henan, the cradle of the most important cultures in China and the seat of several capitals for more than two millennia. There, the high depth of buried remains covered by alluvial deposits and the building materials, mainly made by rammed earth, did not favor the use of geophysics. In this paper, we present and discuss the GPR and ERT prospection we conducted in Kaifeng (Henan, China), nearby a gate of the city walls dated to the Northern Song Dynasty. The integration of GPR and ERT provided useful information for the identification and characterization of archaeological remains buried at different depths. Actually, each geophysical technique, GPR frequency (used for the data acquisition) as well as each way to analyze and visualize the results (from radargrams to time slice) only provided partial information of little use if alone. The integration of the diverse techniques, data processing and visualization enabled us to optimize the penetration capability, the resolution for the detection of archaeological features and their interpretation. Finally, the results obtained from the GPR and ERT surveys were correlated with archaeological stratigraphy, available nearby the investigated area. This enabled us to further improve the interpretation of results from GPR and ERT survey and also to date the anthropogenic layers from Qing to Yuan Dynasty.

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.

One of the most complex challenges of heritage sciences is the identification and protection of buried archaeological heritage in urban areas and the need to manage, maintain and inspect underground services. Archaeology and geophysics, used in an integrated way, provide an important contribution to open new perspectives in understanding both the history of cities and in helping the decision makers in planning and governing the urban development and management. The problems of identification and interpretation of geophysical features in urban subsoil make it necessary to develop ad hoc procedures to be implemented and validated in significant case studies. This paper deals with the results of an interdisciplinary project in Cusco (Peru), the capital of Inca Empire, where the georadar method was applied for the first time in the main square. The georadar method was successfully employed based on knowledge of the historical evolution of Cusco and the availability of archaeological records provided by some excavations nearby the study area. Starting from a model for the electromagnetic wave reflection from archaeological structures and pipes, georadar results were interpreted by means of comparative morphological analysis of high amplitude values observed from time slices with reflectors visualized in the radargrams.

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.