Archaeological Prospection最新文献

In the past decade, numerous studies have successfully mapped thousands of former charcoal production sites (also called relict charcoal hearths) manually using digital elevation model (DEM) data from various forested areas in Europe and the north-eastern USA. The presence of these sites causes significant changes in the soil physical and chemical properties, referred to as legacy effects, due to high amounts of charcoal that remain in the soils. The overwhelming amount of charcoal hearths found in landscapes necessitates the use of automated methods to map and analyse these landforms. We present a novel approach based on open source data and software, to automatically detect relict charcoal hearths in large-scale LiDAR datasets (visualized with Simple Local Relief Model). In addition, the approach simultaneously provides both general as well as domain-specific information, which can be used to further study legacy effects. Different versions of the methodology were fine-tuned on data from north-western Connecticut and subsequently tested on two different areas in Connecticut. The results show that these perform adequate, with F1-scores ranging between 0.21 and 0.76, although additional post-processing was needed to deal with variations in LiDAR quality. After testing, the best performing version of the prediction model (with an average F1-score of 0.56) was applied on the entire state of Connecticut. The results show a clear overlap with the known distribution of charcoal hearths in the state, while new concentrations were found as well. This shows the usability of the approach on large-scale datasets, even when the terrain and LiDAR quality varies.

Geographic information systems (GIS) methods, combined with airborne remote sensing, enable collection of complex spatial datasets across broad regional areas. This article explores the use of GIS techniques for fast collection, processing and analysis of pedestrian survey data. This approach is used at Tell Abu Shusha, a multiperiod site in the Jezreel Valley of northern Israel. Surface survey of this tell and the surrounding region, conducted by the Jezreel Valley Regional Project during 2017, documented extensive visible remains of settlement features as well as the ruins of the Ottoman era village of Abu Shusha. Using this data, the potential for existing spatial analytical techniques to be modified and improved through modern processing capabilities is shown. Kolmogorov–Smirnov nonparametric tests, pure locational (k-means) and unconstrained clustering methods were applied to the field walking survey data, showing evidence of feature clustering at multiple scales as well as environmental patterning in where features are located. Results demonstrate that these approaches increase the speed and accuracy of pedestrian survey data collection and that the modification of these analytical techniques makes them more robust than before, allowing for the identification of meaningful large-scale spatial patterns.

This paper assesses the value of high temporal frequency satellite data with various spatial sampling resolutions for multi-scalar historic environment survey and management use cases in Scotland, specifically for broad-brush landscape characterisation, for monitoring the condition of monuments and for the discovery of otherwise unknown sites. Dealing with a part of the world where applications of satellite imagery are almost entirely unexplored, this study takes a real-world approach, which foregrounds the purpose at hand rather than presenting a case study from an optimal setting. The study highlights the importance of detailed imagery to support interpretation in some instances, and the challenges of obtaining time-critical optical imagery in a part of the world that experiences significant periods of cloud cover. The real-world availability of data in such settings is assessed, highlighting that even with daily revisits, useable imagery cannot be guaranteed. The implications of current and past tasking patterns for availability of high-resolution data now and in the future are discussed. The study identifies the complementary roles that satellite imagery can fulfil, while identifying the limitations that remain to fuller applications of such data, in a study that will be relevant to many parts of Europe and beyond.

Spatial analysis is paramount for understanding, monitoring, and conserving ancient settlements and cultural landscapes. Advancing remote sensing and prospection techniques are expanding the methodological frame of archaeological settlement analysis by enabling remote, landscape-scale approaches to mapping and investigation. Whilst particularly effective in arid lands and areas with sparse or open ground cover, such as vegetation and buildings, these approaches remain peripheral in tropical environments because of technical and contextual challenges. In tropical Eastern Africa, for example, scales, resolution and visibility are often compromised by thick vegetation cover, inadequate access to, if not lack of, imagery resources and technologies, and the availability of comparative archaeological data for interpretation. This paper presents the initial results of spatial analysis, using historic landscape characterisation, remote sensing, published and legacy data, and a pilot ground survey to examine the earliest settlement of Zanzibar, Unguja Ukuu. Comparing multiple strands of evidence in a Geographic Information System (GIS), we use each as a test on the others to draw out the strengths and weaknesses of each technique in the context of tropical and coastal Eastern Africa. Drone photogrammetry, geophysical prospection, and ground survey were compared with legacy remote sensing resources and the results of a coring survey conducted across the site during the 1990s into a GIS platform to produce multi-phase hypothetical maps of the archaeological site in the context of its potential resource landscape. These were then tested against the results of recent excavations. The discussion highlights the challenges and potential of combining these techniques in the context of Eastern Africa and provides some suggested methods for doing so. We show that remote sensing techniques give an insight into current landscapes but are less useful in understanding or modelling how sites would have fitted into their surroundings in the past, when conditions were potentially very different.

Today, the advances in airborne LIDAR technology provide high-resolution datasets that allow specialists to detect archaeological features hidden under wooded areas more efficiently. Still, the complexity and large scale of these datasets require automated analysis. In this respect, artificial intelligence (AI)-based analysis has recently created an alternative approach for interpreting remote sensing data. In this study, a convolutional neural network (CNN) is proposed to detect clearance cairns, which are visible in today's landscape and act as important markers of past agricultural activities. For this aim, the U-shape network architecture is adapted, trained from scratch with an original labelled dataset and tested in various field sites, focusing on southern Sweden. Although it is challenging to tune the hyperparameters and decide on the proper network architecture to obtain reliable prediction, long-running experimental tests with this model produced promising results, with training and validation metrics of 0.8406 Dice-coefficient, 0.7469 Val-dice coefficient, and 0.7350 IuO and 0.6034 Val-IoU values, once trained with the best parameters. Thus, the proposed CNN model in this study made data interpretation quicker and guided scholars to focus on the location of the target objects, opening a new frontier for future landscape analysis and archaeological research.

Remote sensing data covering large geographical areas can be easily accessed and are being acquired with greater frequency. The massive volume of data requires an automated image analysis system. By taking advantage of the increasing availability of data using computer vision, we can design specific systems to automate data analysis and detection of archaeological objects. In the past decade, there has been a rise in the use of automated methods to assist in the identification of archaeological sites in remote sensing imagery. These applications offer an important contribution to non-intrusive archaeological exploration, helping to reduce the traditional human workload and time by signalling areas with a higher probability of presenting archaeological sites for exploration. This survey describes the state of the art of existing automated image analysis methods in archaeology and highlights the improvements thus achieved in the detection of archaeological monuments and areas of interest in landscape-scale satellite and aerial imagery. It also presents a discussion of the benefits and limitations of automatic detection of archaeological structures, proposing new approaches and possibilities.

This article demonstrates the application of the methods unravelling microtopographic features, specifically, sunken linear landforms indicating remains of historical trackways in forests. These are related to the ‘Magna Via’ route in the vicinity of the Deserted Castle and the Peťuša Castle in Central Slovakia. The microtopography validation dataset indicating sunken linear landforms was used to evaluate the data overlap of tracks identified by the global positioning and navigation system (GNSS) and a model of tracks created with the least cost path (LCP) algorithm. Microtopographic features derived from a digital relief model generated by high-resolution Light Detection and Ranging (LiDAR) scans (density of 5 points/pixel) allowed us to determine the exact position (submeter total vertical and horizontal accuracy) of certain segments of the GNSS tracks and LCP overlapping the sunken linear landforms. Moreover, the LCP model shows the most efficient trackways considering the travel costs depending on the slope parameter.

Ground Penetrating Radar (GPR) survey was conducted to detect historic unmarked graves from the period of the Civil War (1861–1865) at the Fairlawn Cemetery in Stillwater, Oklahoma. The GPR survey at the Fairlawn Cemetery will help preserve the unmarked historic graves if they exist or clear sections of the cemetery for possible expansion. GPR detection of historic graves are often a challenge as these graves are made of wooden boxes, bones and coffins, with no metal caskets or concrete burial vaults. It was even more challenging to detect unmarked graves in this study as the cemetery is covered with iron-rich silty clay soil, which attenuates the GPR signals. We conducted the GPR survey along a grid consisting of 44 parallel 30-m-long profiles spaced at 50-cm intervals using the 400-MHz antenna. The acquired GPR data were processed as 2D profiles and produced a pseudo-3D GPR volume to resolve the unmarked graves. Multiple features extracted from the pseudo-3D volume at depths ranging from 0.7 to 1.3 m aligned along three north–south rows. Based on the dimensions, orientation, distribution and depth of burial of the anomalous features relative to the recent graves, we interpreted these features as unmarked graves. This study has demonstrated the GPR as an effective non-invasive technique in detecting historical unmarked graves that contain no metal caskets or concrete burial vaults. This work will contribute not only to the science of historical archaeology but also to prehistorical archaeology, as caskets were not typically part of the prehistorical burials, and the modern-day archaeology, particularly in the cases of mass graves in recent conflicts.

Until recently, evidence of Neanderthal cave use in the Algarve (Southern Portugal) came only from the site of Ibn Ammar. Over the last couple of years, archaeological excavations inside another cave, Gruta da Companheira, yielded Mousterian stone tools associated with possible human fossils. The discovery of this assemblage is groundbreaking because it may contribute to enlighten the Neanderthal/cave relationship and explain the dearth of similar sites in the Algarve. Gruta da Companheira, however, is a complex karst system, which was partly destroyed during its accidental discovery. As result, the original entrance of the cave remains unknown, and it is unclear how sediments, archaeological materials and Neanderthals accessed the site. To tackle these issues, we combined geomorphological observations with speleological, Electrical Resistivity Tomography (ERT) and Ground-Penetrating Radar (GPR) prospections. Our data indicate that Gruta da Companheira was probably accessed from the hilltop through a sub-vertical entrance. Additionally, our results suggest the existence of yet unexplored shallower cavities connected with the already known passages of Gruta da Companheira. These results will guide the opening of new excavation areas at the site. The limestone bedrock hosting Gruta da Companheira has been extensively dissolved by karst processes. Therefore, it is necessary to focus future research on the cave infillings to clarify whether Neanderthals exploited the cave's inner chambers or alternatively limited their occupations to the hilltop and geogenic processes reworked their materials into the endokarst system shortly after their stays. The deep karstification and partial collapse of the hill hosting Gruta da Companheira are common in limestone outcrops that occur throughout the Western Algarve. Poor visibility and poor accessibility of this karst area, densely covered with shrubby vegetation, are factors that need to be considered when addressing the scarcity of Middle Palaeolithic cave sites in this region.

In the area of Western Ukraine, some aspects of mound (barrow) chronology might be resolved by using non- or minimally invasive archaeological prospection. As the cemeteries usually comprise two temporal units—the Late Neolithic Corded Ware Culture (third millennium BC) and Middle Bronze Age Komarów culture (second millennium BC)—by referring to particular funerary rites Komarów culture and their magnetic reflection, they can be distinguished by means of magnetometry survey supported with verification drilling. Recognition of the internal structure of the cemeteries is essential when confronted with their vast occurrence in Western Ukraine and also the morphological similarity of mounds. However, due to the great diversity in known grave architecture of the Komarów culture, it is essential to stress that the methodological approach presented here applies only to the specific construction type of burnt wooden and clay structures, which rarely appear in Corded Ware Culture funerary practices. In addition to excavation, the applied methods provided preliminary information on the cultural affiliation and time of the construction of two mounds in Myluvannia in the pre-excavation stage of research. In this light, it is therefore possible to assess the potential occurrence of Komarów culture mounds in other Western Ukrainian mound cemeteries.