Search Results (54)

Underwater cultural heritage represents a fragile and largely unexplored component of historical landscapes, particularly in dynamic fluvial and coastal environments. Despite increasing international attention to its protection, the spatial identification of submerged heritage remains methodologically challenging. This study proposes a geo-historical approach that integrates historical cartography and Geographic Information Systems (GIS) to identify areas of high archaeological potential in underwater contexts. Focusing on the Douro River in Porto (Portugal), a UNESCO World Heritage city with a long maritime and fluvial history, the research analyses a set of key historical maps from the eighteenth and nineteenth centuries, complemented by documentary and archaeological sources. These cartographic materials were georeferenced and critically assessed in QGIS, enabling the digitisation of features associated with land–water interaction, navigation hazards, port infrastructures, and military defences. The resulting spatial dataset was used to generate an interpretative map and a kernel density model highlighting potential underwater heritage hotspots along the riverbed and riverbanks. The findings identify several priority zones, including the river mouth, historic quays, former shipbuilding areas, and sectors linked to nineteenth-century defensive structures. While the study does not include in situ verification, it demonstrates the value of historical maps as predictive tools for guiding targeted underwater surveys and proposes a transferable, cost-effective framework for heritage prospection and management in historically active fluvial–estuarine settings. Full article

Dispersed archaeological landscapes are often rich in heritage value but weakly integrated into regional tourism systems. This creates difficulties in visitor orientation, interpretive continuity, and conservation-sensitive tourism planning. In response to this problem, this study examines the Adana–Kozan–Anavarza axis in southern Türkiye and proposes a spatial corridor framework for organising tourism development within a dispersed archaeological landscape. The research integrates spatial accessibility assessment, service-capacity evaluation, field observation, and sequential route design in order to establish a hierarchical gateway–transition–anchor configuration. Anavarza, one of the largest archaeological complexes of Cilicia, represents a monumental urban heritage site and a biocultural landscape situated within a Mediterranean ecological zone historically associated with Pedanius Dioscorides. Although current visitor volumes remain moderate, official statistics indicate a substantial increase in annual entries between 2022 and 2024, reflecting rising destination visibility. This emerging growth trajectory underscores the need for proactive spatial governance mechanisms prior to the onset of congestion and environmental degradation pressures. The findings suggest that Adana can function as a metropolitan gateway, Kozan as an intermediate staging node, and Anavarza as the archaeological anchor within a realistic multi-day visitor sequence. In this configuration, visitor functions are distributed across multiple nodes, while the ecological and archaeological sensitivity of the anchor landscape is more cautiously managed through spatial sequencing. Rather than proposing a predictive model, the study develops and assesses a context-responsive spatial planning framework grounded in accessibility, infrastructural feasibility, and conservation-sensitive visitor distribution. Beyond the local case, the study offers a transferable hierarchical staging logic for corridor-based heritage planning. Full article

Climate change has among its effects the increasing frequency and intensity of natural disasters, such as landslides, floods, erosion and fires, with clear implications on both natural and anthropic hazards and risks. These natural phenomena pose a growing threat to archaeological heritage through increased rates of soil erosion, flooding, and landslides. This study presents a multidisciplinary approach to assess the erosion risk affecting medieval rural settlements in the Basilicata Region of Southern Italy. This area is characterised by high-impact natural phenomena that have influenced settlement patterns in the past. The focus is on rural settlements that arose during the Middle Ages, some of which were abandoned as early as the late Middle Ages. This study has the dual objective of analysing the natural causes that may have led to the abandonment of many sites in ancient times and producing a predictive multi-risk map of the possible loss of cultural heritage sites. By integrating archaeological data, remote sensing, historical sources, and geospatial modelling, a multi-risk map was developed to identify areas at the highest risk. The results demonstrate the urgent need for proactive conservation strategies in the face of ongoing climatic change. Full article

This review synthesizes advances in nondestructive DNA extraction from teeth, emphasizing their importance in forensics and archaeogenetics. Because of their mineralized structure and resistance to diagenesis, teeth remain vital for human identification when other tissues are unavailable or degraded. Modern protocols targeting dental cementum have shown high success rates in retrieving nuclear DNA while maintaining specimen integrity, supporting ethical standards, and enabling additional morphological and isotopic analyses. Nondestructive extraction methods produce DNA yields comparable to—or in some archaeological cases, greater than—those of traditional destructive approaches, while ensuring strict contamination control and minimal physical impact. Cementum is a reliable source of DNA in aged and degraded teeth, although the petrous part of the temporal bone still represents the best option under extreme preservation conditions. These results highlight the need for context-specific sampling strategies that balance analytical goals with the preservation of museum collections. Future efforts include testing nondestructive protocols across various forensic scenarios and creating predictive models for DNA preservation. Overall, these developments promote ethical, effective, and sustainable practices in human genomic analysis. Full article

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. Full article

During the processes of excavation, restoration, and conservation of archaeological sites, it is common practice to perform physical and chemical characterization of the site materials. This is carried out to determine the best methods and materials for conserving and preserving the site. For this reason, techniques such as infrared spectroscopy and elemental analysis by X-ray fluorescence (XRF) are primarily used for chemical characterization, while mechanical tests such as the uniaxial compression test and hardness tests are used for physical and mechanical characterization. However, a common limitation is obtaining samples for destructive physical tests, such as compression tests, due to their invaluable cultural value. To address this problem, this work proposes the mechanical characterization of the material through nanoindentation. This technique requires a smaller sample size and can be performed in a timely manner by observing the resistance of each mineralogical phase present in the material. Thus, a preliminary predictive model of mechanical resistance is proposed based on the composition observed in the samples from the archaeological site of Cerro de los Remedios, located in the municipality of Comonfort, Guanajuato, Mexico. The samples were characterized using infrared spectroscopy, XRF, XRD, and SEM-EDS. The results indicate that the stone (caliche) is formed from 95.6–93% micrite calcite; 2.51–0.42% aluminosilicate; 3.14–1.89% high-calcium aluminosilicate; and 3.43–2.39 quartz or amorphous SiO₂. The proposed correlation models were adjusted to a linear function, a second-order polynomial, and a logarithmic function. In the M2–linear model, the non-linear effects generated by variables such as texture, porosity, phase adhesion, cement type, and cracks or discontinuities were not considered. In this model the best prediction of the experimental data was obtained within a variation of ±15%. Full article

Groundwater level monitoring is crucial for assessing hydrological responses to climate change and human activities, which pose significant threats to the sustainability of semi-arid aquifers and the cultural heritage they sustain. This study presents an integrated remote sensing and transformer-based deep learning framework that combines diverse geospatial datasets to predict spatiotemporal variations across the plain near the Persepolis and Naqsh-e Rustam archaeological complexes—UNESCO World Heritage Sites situated at the plain’s edge. We assemble 432 synthetic aperture radar (SAR) scenes (2015–2022) and derive vertical ground motion rates greater than −180 mm yr⁻¹, which are co-localized with multisource geoinformation, including hydrometeorological indices, biophysical parameters, and terrain attributes, to train transformer models with traditional deep learning methods. A sparse probabilistic transformer (ConvTransformer) trained on 95 gridded variables achieves an out-of-sample R² = 0.83 and RMSE = 6.15 m, outperforming bidirectional deep learning models by >40%. Scenario analysis indicates that, in the absence of intervention, subsidence may exceed 200 mm per year within a decade, threatening irreplaceable Achaemenid stone reliefs. Our results indicate that attention-based networks, when coupled to synergistic geodetic constraints, enable early-warning quantification of groundwater stress over heritage sites and provide a scalable template for sustainable aquifer governance worldwide. Full article

The provision of food in pre-Inca/Inca cultures (1000 BC–≈1532 AD) in environments near Lake Titikaka (approximately 4000 m above sea level) was possible through an agricultural technique called “Waru-Waru”, which consists of filling the space (volume) between rows of land containing plants that are cultivated (a series of earth platforms surrounded by water canals) with water, using water as thermal energy storage to store energy during the day and to regulate the temperature of the soil and crop atmosphere at night. The problem is that these cultures left no evidence in written documents that have been preserved to this day indicating the mathematical models, the physics involved, and the experimental part they performed for the research, development, and innovation of the “Waru-Waru” technique. From a review of the existing literature, there is (1) bibliography that is devoted to descriptive research (about the geometry, dimensions, and shapes of the crop fields (and more based on archaeological remains that have survived to the present day) and (2) studies presenting complex mathematical models with many physical parameters measured only with recently developed instrumentation. The research objectives of this paper are as follows: (1) develop a mathematical model that uses finite differences in fluid mechanics, thermodynamics, and heat transfer to explain the experimental and theory principles of this pre-Inca/Inca technique; (2) the proposed mathematical model must be in accordance with the mathematical calculation tools available in pre-Inca/Inca cultures (yupana and quipu), which are mainly based on arithmetic operations such as addition, subtraction, and multiplication; (3) develop a mathematical model in a sequence of steps aimed at determining the best geometric form for thermal energy storage and plant cultivation and that has a simple design (easy to transmit between farmers); (4) consider the assumptions necessary for the development of the mathematical model from the point of view of research on the geometry of earth platforms and water channels and their implantation in each cultivation area; (5) transmit knowledge of the construction and maintenance of “Waru-Waru” agricultural technology to farmers who have cultivated these fields since pre-Hispanic times. The main conclusion is that, in the mathematical model developed, algebraic mathematical expressions based on addition and multiplication are obtained to predict and explain the evolution of soil and water temperatures in a specific crop field using crop field characterization parameters for which their values are experimentally determined in the crop area where a “Waru-Waru” is to be built. Therefore, the storage of thermal energy in water allows crops to survive nights with low temperatures, and indirectly, it allows the interpretation that the Inca culture possessed knowledge of mathematics (addition, subtraction, multiplication, finite differences, approximation methods, and the like), physics (fluids, thermodynamics, and heat transfer), and experimentation, with priority given to agricultural techniques (and in general, as observed in all archaeological evidence) that are in-depth, exact, practical, lasting, and easy to transmit. Understanding this sustainable energy storage technique can be useful in the current circumstances of global warming and climate change within the same growing areas and/or in similar climatic and environmental scenarios. This technique can help in reducing the use of fossil or traditional fuels and infrastructure (greenhouses) that generate heat, expanding the agricultural frontier. Full article

Pottery is one of the most common and abundant types of human remains found in archaeological contexts. The analysis of archaeological pottery involves the reconstruction of pottery vessels from their sherds, which represents a laborious and repetitive task. In this work, we investigate a deep learning-based approach to make that process more efficient, accurate, and fast. In that regard, given a sherd’s digital point cloud in a standard, so-called canonical position, the proposed method predicts the geometric transformation, which moves the sherd to its expected normalized position relative to the vessel’s coordinate system. Among the main components of the proposed method, a pair of deep 1D convolutional neural networks trained to predict the 3D Euclidean transformation parameters stands out. Herein, rotation and translation components are treated as independent problems, so while the first network is dedicated to predicting translation moments, the other infers the rotation parameters. In practical applications, once a vessel’s shape is identified, the networks can be trained to predict the target transformation parameter values. Thus, given a 3D model of a complete vessel, it may be virtually broken down countless times for the production of sufficient data to meet deep neural network training demands. In addition to overcoming the scarcity of real sherd data, given a virtual sherd in its original position, that procedure provides paired canonical and normalized point clouds, as well as the target Euclidean transformation. The herein proposed 1D convolutional neural network architecture, the so-called PotNet, was inspired by the PointNet architecture. While PointNet was motivated by 3D point cloud classification and segmentation applications, PotNet was designed to perform non-linear regressions. The method is able to provide an initial estimate for the correct position of a sherd, reducing the complexity of the problem of fitting candidate pairs of sherds, which could be then carried out by a classical adjustment method like ICP, for instance. Experiments using three distinct real vessels were carried out, and the reported results suggest that the proposed method can be successfully used for aiding pottery reconstruction. Full article

The thermoluminescence (TL) method is one of the most widely used techniques in various studies, including dosimetric applications, dating of archaeological and geological materials, luminescence spectroscopy of certain insulating or semiconducting phosphors, and the detection of ionizing radiation damage. This study examines the TL properties of plagioclase, a feldspar group mineral, focusing on its dose–response behavior, kinetic parameters, and glow curve characteristics. TL measurements of plagioclase samples were carried out with different ionizing radiation doses ranging from 0.1 to 550 Gy. The results show a strong linear dose–response relationship in the 0.3–550 Gy range, with no evidence of saturation or supralinearity. A computerized glow curve deconvolution (CGCD) analysis revealed that the TL glow curve of the mineral consists of five distinct TL peaks with activation energies ranging from 0.842 eV to 0.890 eV and obeying general order kinetics. In addition, an artificial neural network (ANN) model was developed to predict TL glow curves using three optimization algorithms, including Levenberg–Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugate Gradient (SCG). Among these, the BR algorithm demonstrated the best performance with an accuracy value of 0.99915, a Mean Absolute Error (MAE) of 2.34 × 10⁻³, and a Mean Squared Error (MSE) of 3.82 × 10⁻⁵, outperforming LM and SCG in in terms of generalization and accuracy. The findings of this study demonstrate the effectiveness of combining TL analysis with ANN-based modelling for accurate dose–response predictions and the improved luminescence characterization of plagioclase, supporting the applications of luminescence studies in radiation dosimetry and geochronology. Full article

The conservation and monitoring of archaeological sites submerged in water reservoirs have become increasingly necessary in a climatic context where water management policies are possibly accelerating erosion and sedimentation processes. This study assesses the potential of using multitemporal LiDAR data and Machine Learning (ML)—specifically the XGBoost algorithm—to predict erosional and sedimentary processes affecting archaeological sites in the Valdecañas Reservoir (Spain). Using data from 2010 to 2023, topographic variations were calculated through a robust workflow that included the co-registration of LiDAR point clouds and the generation of high-resolution DEMs. Hydrological variables, topographic descriptors, and water dynamics-related factors were extracted and used to train models based on the detected measurement errors and the temporal ranges of the DEMs. The model trained with 2018–2023 data exhibited the highest predictive performance (R² = 0.685), suggesting that sedimentary and erosional patterns are partially predictable. Finally, a multicriteria approach was applied using a DEM generated from 1957 aerial photographs to estimate past variations based on historical terrain conditions. The results indicate that areas exposed to fluctuating water levels and different topographic orientations suffer greater damage. This study highlights the value of LiDAR and ML in assessing the vulnerability of archaeological sites in highly dynamic environments. Full article

Coastal lagoons are important wetland sites for migratory species and the local flora and fauna population. The Stagnone Lagoon is a coastal lagoon located on the west edge of Sicily between the towns of Marsala and Trapani. The area is characterized by salt-harvesting plants and several archaeological sites and is affected by microtidal excursion. Two mouths allow exchange with the open sea: one smaller and shallower in the north and one larger and deeper in the south. This study aims to understand the lagoon’s hydrodynamics, in terms of circulation and involved forces. The circulation process appears to be dominated mainly by tide excursions and wind forces. Wind velocity, water levels, and water velocity were recorded during different field campaigns in order to obtain a benchmark value. The hydrodynamic circulation has been studied with a 2DH (two-dimensional in the horizontal plane) unstructured mesh model, calibrated with data collected during the 2006 field campaign and validated with the data of the 2007 campaign. Rapid changes in averaged velocity have been found both in Vx and Vy components, showing the strong dependence on seiches. This study tries to identify the main factor that domains the evolution of the water circulation. Sensitivity analyses were conducted to estimate the correct energy transfer between the forcing factors and dissipating ones. A Gauckler–Strickler roughness coefficient between 20 and 25 m^1/3/s is found to be the most representative in the lagoon. To enhance the knowledge of this peculiar lagoon, the MIKE 21 model has been used, reproducing all the external factors involved in the circulation process. Nash–Sutcliffe coefficient of efficiency (NSE) values up to 0.92 and 0.79 are reached with a Gauckler–Strickler coefficient equal to 20 m^1/3/s related to water depth and the Vy velocity component. The Vx velocity component NSE has never been satisfying, showing the limits of the 2D approach in reproducing the currents induced by local morphological peculiarities. Comparing the NSE value of water depth, there is a loss of up to 70% in model predictivity capability between the southern and the northern lagoon areas. This study aims to support the local decision-makers to improve the management of the lagoon itself. Full article

Ancient stadia and circuses were considered by Greeks and Romans to be excellent places for live events. Back in ancient times, many people participated in public entertainment from athletic games, as typical of Greek traditions, to combats between gladiators and wild beasts. Among all of them, the most acclaimed were the horse races conducted with chariots, and this was the main sport of ancient Roman stadia. This paper deals with the digital reconstruction of three stadia belonging to the 2nd century B.C. (i.e., Panathenaic Stadium) and to the 1st century A.D. (i.e., Circus Maximum and Stadium of Domitian). The digital models have been rebuilt based on historical resources and archaeological discoveries to conduct the acoustic simulations and understand the acoustic behavior within these places. After the assessment of the main acoustic parameters, the noise levels from crowds have been predicted in different ways: based on information gathered from historic annals, and the comfort used for modern stadia to predict the ancient conditions with reference to the crowd noise levels measured in modern stadiums. The results indicate that the acoustic response of ancient stadia is very similar to the modern ones, in terms of both reverberation and noise level from crowds. Full article

This study explores the synergistic integration of remote sensing (RS) and Historical Landscape Characterisation (HLC) methodology as an innovative, multi-scalar and holistic approach to enhance archaeological planning. The goal is to maximize the effectiveness of the investigations, optimizing data collection and improving the contextual understanding of the sites. In fact, these methodologies can significantly contribute to the documentation, conservation, planning and valorisation of archaeological areas. By integrating RS data with features detected by HLC, a complete picture is obtained that facilitates a deeper understanding of the landscape and historical dynamics. This article will explain the combined approach of RS and HLC, presenting some methodologies key to improving the precision and effectiveness of archaeological planning. This integration facilitates the sustainable preservation of archaeological resources and contributes to the conscious management of cultural heritage in the context of contemporary development. The paper demonstrates, through a case study, how the application of the two methodologies (RS and HLC) in an integrated form can provide an exhaustive interpretation of the territory in which the archaeological area is located, which can represent an exhaustive knowledge base on which to set up effective processes for the strategic territorial planning of archaeological areas. Full article

Traditional GIS-based statistical models are intended to extrapolate patterns of settlements and their interactions with the environment. They contribute significantly to our knowledge of past human–land relationships. Yet, these models are often criticized for their empiricism, lopsided specific factors, and for overlooking the synergy between variables. Though largely untested, machine learning and artificial intelligence methods have the potential to overcome these shortcomings comprehensively and objectively. The northeastern Tibetan Plateau (NETP) is characterized by diverse environments and significant changes to the social system from the Neolithic to Bronze Age. In this study, this area serves as a representative case for assessing the complex relationships between settlement locations and geographic environments, taking full advantages of these new models. We have explored a novel modeling case by employing GIS and random forests to consider multiple factors, including terrain, vegetation, soil, climate, hydrology, and land suitability, to construct classification models identifying environmental variation across different cultural periods. The model exhibited strong performance and a high archaeological prediction value. Potential living maps were generated for each cultural stage, revealing distinct environmental selection strategies from the Neolithic to Bronze Age. The key environmental parameters of elevation, climate, soil erosion, and cultivated land suitability were calculated with high weights, influencing human environmental decisions synergistically. Furthermore, we conducted a quantitative analysis of temporal dynamics in climate and subsistence to understand driving mechanisms behind environmental strategies. These findings suggest that past human environmental strategies were based on the comprehensive consideration of various factors, coupled with their social economic scenario. Such subsistence-oriented activities supported human beings in overcoming elevation limitation, and thus allowed them to inhabit wider pastoral areas. This study showcases the potential of machine learning in predicting archaeological probabilities and in interpreting the environmental influence on settlement patterns. Full article