Africa represents a vast region where remote sensing technologies have been largely uneven in their archaeological applications. With impending climate-related risks such as increased coastal erosion and rising sea levels, coupled with rapid urban development, gaps in our knowledge of the human history of this continent are in jeopardy of becoming permanent. Spaceborne and aerial remote sensing instruments are powerful tools for producing relatively complete records of archaeological settlement patterns and human behavior at landscape scales. These sensors allow for massive amounts of information to be recorded and analyzed in short spans of time and offer an effective means to increase survey areas and the discovery of new cultural deposits. In this paper, we review various case studies throughout Africa dealing with aerial and satellite remote sensing applications to landscape archaeology in order to highlight recent developments and future research avenues. Specifically, we argue that (semi)automated remote sensing methods stemming from machine learning developments will prove vital to expanding our knowledge base of Africa’s archaeological record. This is especially important for coastal and island regions of the continent where climate change threatens the survival of much of the archaeological record.
Remote sensing has become the most important data source for the digital elevation model (DEM) generation. DEM analyses can be applied in various fields and many of them require appropriate DEM visualization support. Analytical hill-shading is the most frequently used relief visualization technique. Although widely accepted, this method has two major drawbacks: identifying details in deep shades and inability to properly represent linear features lying parallel to the light beam. Several authors have tried to overcome these limitations by changing the position of the light source or by filtering. This paper proposes a new relief visualization technique based on diffuse, rather than direct, illumination. It utilizes the sky-view factor—a parameter corresponding to the portion of visible sky limited by relief. Sky-view factor can be used as a general relief visualization technique to show relief characteristics. In particular, we show that this visualization is a very useful tool in archaeology as it improves the recognition of small scale features from high resolution DEMs.
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 contribution proposes an evaluation of lidar and radar data processing and its potential in revealing archaeological features within a level plain environment, the southern lowland of Verona (Italy), focusing on evidences dating back to the Bronze Age. Many archaeological sites in the research area, including some of the most outstanding settlements of Terramare Culture, were identified or at least examined through aerial photo observation. Even if in several occasions modern agricultural activities contributed to the discoveries, bringing to the surface artifacts and scrapes of buried layers, this kind of impact has also been progressively deteriorating the archaeological record, hence the proto-historic landscape is now discernible through evanescent marks which cannot be always detected using customary optical sensors. Lidar and radar data analysis has then been considered as an alternative, non-invasive method of investigation on such a vast area.
This paper focuses on the potential of an integrated approach using aerial LiDAR, aerial and terrestrial photogrammetry, terrestrial laser scanning, and archaeological survey to detect the presence and configuration of lost medieval settlements under canopy. This approach was applied to the site of Altanum (Calabria, Italy), on the hill of Sant’Eusebio, completely covered by vegetation. Altanum was a large fortified settlement characterised by a long occupation, especially during the Byzantine and Norman-Swabian periods. The activity began by carrying out a LiDAR survey of the whole hill. The acquired LiDAR data were processed and filtered in order to obtain a DFM (Digital Feature Model) useful for the identification of features of archaeological interest. Several enhancement techniques were performed on DFM to increase the visibility of archaeological features. The features thus identified were subsequently surveyed through the use of terrestrial and aerial photogrammetry integrated with laser scanning to document the visible buildings. The most significant result of the study was to create a single GIS platform with the integration of all data in order to delineate the whole settlement layout, as well as to produce 2D and 3D datasets useful for the for knowledge and protection of the identified remains.
This paper discusses how the use of AI (artificial intelligence) detected later prehistoric field systems provides a more reliable base for reconstructing palaeodemographic trends, using the Netherlands as a case study. Despite its long tradition of settlement excavations, models that could be used to reconstruct (changes in) prehistoric land use have been few and often relied on (insufficiently mapped) nodal data points such as settlements and barrows. We argue that prehistoric field systems of field plots beset on all sides by earthen banks—known as Celtic fields—are a more suitable (i.e. less nodal) proxy for reconstructing later prehistoric land use. For four 32.25 km2 case study areas in different geogenetic regions of the Netherlands, prehistoric land use surface areas are modelled based on conventional methods and the results are compared to the results we obtained by using AI-assisted detection of prehistoric field systems. The nationally available LiDAR data were used for automated detection. Geotiff DTM images were fed into an object detection algorithm (based on the YOLOv4 framework and trained with known Dutch sites), and resultant geospatial vectors were imported into GIS. Our analysis shows that AI-assisted detection of prehistoric embanked field systems on average leads to a factor 1.84 increase in known surface areas of Celtic fields. Modelling the numbers of occupants from this spatial coverage, yields population sizes of 37–135 persons for the case study regions (i.e. 1.15 to 4.19 p/km2). This range aligns well with previous estimates and offers a more robust and representative proxy for palaeodemographic reconstructions. Variations in land use coverage between the regions could be explained by differences in present-day land use and research intensity. Particularly the regionally different extent of forestlands and heathlands (ideal for the (a) preservation and (b) automated LiDAR detection of embanked field systems) explains minor variations between the four case study regions.
This paper presents an algorithm for large-scale automatic detection of burial mounds, one of the most common types of archaeological sites globally, using LiDAR and multispectral satellite data. Although previous attempts were able to detect a good proportion of the known mounds in a given area, they still presented high numbers of false positives and low precision values. Our proposed approach combines random forest for soil classification using multitemporal multispectral Sentinel-2 data and a deep learning model using YOLOv3 on LiDAR data previously pre-processed using a multi–scale relief model. The resulting algorithm significantly improves previous attempts with a detection rate of 89.5%, an average precision of 66.75%, a recall value of 0.64 and a precision of 0.97, which allowed, with a small set of training data, the detection of 10,527 burial mounds over an area of near 30,000 km2, the largest in which such an approach has ever been applied. The open code and platforms employed to develop the algorithm allow this method to be applied anywhere LiDAR data or high-resolution digital terrain models are available.
An interdisciplinary study of the ancient landscape of the Trieste Karst (north-eastern Italy) is presented in this paper. Airborne Laser Scanning (ALS) has been applied to obtain high-resolution topography of the 25 km2 investigated area in order to identify potential archaeological anomalies. The ALS-derived high-resolution Digital Terrain Models have been visualized and managed using QGIS and Relief Visualization Toolbox. Possible archaeological anomalies have been verified through field surveys and interpreted using a multidisciplinary approach mainly based on the collection of associated archaeological materials and geomorphological and stratigraphic evidence. From a methodological perspective, the elaboration and study of ALS-derived images, and in particular the local relief model visualization, combined with the collection of Roman shoe hobnails, have proven to be effective approaches for the certain identification and dating of Roman roads in karst environments. The obtained results have revealed an almost completely unknown Roman landscape: the investigated area was crossed by important public roads, whose layout has been accurately reconstructed for a total length of over 10 km, and occupied by large country estates, sometimes enclosed within boundary walls perfectly fitting the Roman land division grid. One of the identified buildings could correspond to a road station, perhaps the Avesica known from ancient itinerary documents—i.e., the itinerarium Antonini Augusti—due to its position and proximity to a major road junction.
Lidar (Light Detection and Ranging), which has recently come into use for airborne environmental monitoring, is now beginning to find success in archaeological survey. Liaison between the Environment Agency and English Heritage has led to a lidar survey of the Stonehenge landscape, where new sites have been discovered, known ones extended and its potential as an important new tool for the management of archaeological landscapes is discussed for the first time. Lidar has the potential to radically transform our future understanding and management of the historic environment. The article by Devereux et al. (pages 648-660 of this volume) shows the technique applied to woodland.
Lidar (a type of airborne laser scanning) provides a powerful technique for three-dimensional mapping of topographic features. It is proving to be a valuable tool in archaeology, particularly where the remains of structures may be hidden beneath forest canopies. Canuto et al. present lidar data covering more than 2000 square kilometers of lowland Guatemala, which encompasses ancient settlements of the Classic Maya civilization (see the Perspective by Ford and Horn). The data yielded population estimates, measures of agricultural intensification, and evidence of investment in landscape-transforming infrastructure. The findings indicate that this Lowland Maya society was a regionally interconnected network of densely populated and defended cities, which were sustained by an array of agricultural practices that optimized land productivity and the interactions between rural and urban communities.
Although aerial lidar has proven to be a powerful tool for mapping archaeological landscapes, particularly in forested regions of the world, the high costs of conventional lidar acquisition from aircraft or professional-grade drones remains a hurdle to many researchers. The recent development of ultra-compact, relatively low-cost lidar mapping systems that can be deployed on consumer-grade drones now make it feasible for archaeologists to collect their own high-resolution aerial lidar of sites and landscapes, but the efficacy of these systems remains largely untested. This paper presents results of surveys undertaken using a ultra-compact, drone-deployed lidar at archaeological sites located in three different environments: 1) tropical forests at Kealakekua Bay State Historic Park, Hawai’i, 2) piñon-juniper forest on Mesa Verde’s North Escarpment, Colorado, and 3) mixed deciduous-evergreen forest at Enfield Shaker Village, New Hampshire. Results reveal a wealth of archaeological features at the three study sites and demonstrate the potential of drone-based lidar as a tool in archaeological prospection, but also illustrate some of the significant technical and practical challenges involved in making use of this exciting emerging technology.
LiDAR and its derived elevation models have revolutionized archaeological research in forested areas around the globe. Almost a third of Switzerland is covered in forests. The number of archaeological sites recorded in forests in Switzerland is, however, limited. Given these circumstances, it is surprising how underutilized LiDAR data are in archaeological research in the country. As the Federal Office of Topography swisstopo is finalizing the acquisition of new LiDAR datasets, increasing the covered area and allowing for limited time series analyses, these data should be used to the fullest extent. This article describes the open access datasets and elaborates on their potential for archaeological research and cultural heritage management. By employing LiDAR data on a large scale, Swiss archaeological research would likely substantially increase the number of recorded heritage sites. Additionally, this will have the effect of complementing the palimpsests of past anthropogenic activity throughout the landscape while reducing survey biases in the archaeological record.
Remote sensing technologies have helped to revolutionize archaeology. LiDAR (light detection and ranging), a remote sensing technology in which lasers are used as topographic scanners that can penetrate foliage, has particularly influenced researchers in the field of settlement or landscape archaeology. LiDAR provides detailed landscape data for broad spatial areas and permits visualization of these landscapes in ways that were never before possible. These data and visualizations have been widely utilized to gain a better understanding of historical landscapes and their past uses by ancient peoples.
Previous archaeological mapping work on the successive medieval capitals of the Khmer Empire located at Angkor, in northwest Cambodia (∼9th to 15th centuries in the Common Era, C.E.), has identified it as the largest settlement complex of the preindustrial world, and yet crucial areas have remained unmapped, in particular the ceremonial centers and their surroundings, where dense forest obscures the traces of the civilization that typically remain in evidence in surface topography. Here we describe the use of airborne laser scanning (lidar) technology to create high-precision digital elevation models of the ground surface beneath the vegetation cover. We identify an entire, previously undocumented, formally planned urban landscape into which the major temples such as Angkor Wat were integrated. Beyond these newly identified urban landscapes, the lidar data reveal anthropogenic changes to the landscape on a vast scale and lend further weight to an emerging consensus that infrastructural complexity, unsustainable modes of subsistence, and climate variation were crucial factors in the decline of the classical Khmer civilization.
Visualization products computed from a raster elevation model still form the basis of most archaeological and geomorphological enquiries of lidar data. We believe there is a need to improve the existing visualizations and create meaningful image combinations that preserve positive characteristics of individual techniques. In this paper, we list the criteria a good visualization should meet, present five different blend modes (normal, screen, multiply, overlay, luminosity), which combine various images into one, discuss their characteristics, and examine how they can be used to improve the visibility (recognition) of small topographical features. Blending different relief visualization techniques allows for a simultaneous display of distinct topographical features in a single (enhanced) image. We provide a “recipe” and a tool for a mix of visualization techniques and blend modes, including all the settings, to compute a visualization for archaeological topography that meets all of the criteria of a good visualization.
This paper focuses on the detection and spatial characterization of microtopographic relief linked to archaeological remains using full-waveform (FW) Airborne Laser Scanning (ALS). ALS is an optical measurement technique for obtaining high-precision information on the Earth's surface including basic terrain mapping, such as Digital Terrain Model (DTM) and Digital Surface Model (DSM). In the field of cultural heritage management, ALS can provide detailed information useful for feature extraction, but the detection of archaeological microtopographic relief is still a challenge especially for vegetated and highly sloped areas. The investigation was carried out for the archaeological area of Monte Irsi (Southern Italy) characterized by dense herbaceous cover and complex topographical and morphological features, which make air/space prospection very difficult. Results from our investigations pointed out that ALS is a valuable data source to detect and map cultural features even under dense vegetation.
Since the end of the First World War, material remains of the conflict have undergone progressive transformations. In the last decades, conflict archaeology has contributed to the investigation of these remains using a scientific approach. We present the results of a comprehensive study of two Austro-Hungarian trench systems along the northern Italian Front, one located in the area of Millegrobbe, Trento, and the other, part of the Winterstellung in Rotzo, Vicenza. Our aim is to determine the conservation rate of the trench portions still present in the test sites and to investigate the natural and anthropic contributions in the processes of obliteration of the WWI infra(structures) and the restoration of the pre-war landscape. Methodologically, the study was carried out with different degrees of impact: noninvasive, remote sensing techniques (applied to both case studies) and (semi)automatic recognition of the visible war-related traces (only in Millegrobbe); field surveys to verify the reliability of the remotely sensed investigations and the preservation of the structures (both case studies); and microinvasive excavations (only for the Winterstellung) to identify the pre- sin- and postdepositional processes of natural and man-made origin that caused the heterogenous degree of preservation of the investigated structures.
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.
Sixty-six new archaeological sites have been discovered thanks to the combined use of different remote sensing techniques and open access geospatial datasets (mainly aerial photography, satellite imagery, and airborne LiDAR). These sites enhance the footprint of the Roman military presence in the northern fringe of the River Duero basin (León, Palencia, Burgos and Cantabria provinces, Spain). This paper provides a detailed morphological description of 66 Roman military camps in northwestern Iberia that date to the late Republic or early Imperial eras. We discuss the different spatial datasets and GIS tools used for different geographic contexts of varied terrain and vegetation. Finally, it stresses out the relevance of these novel data to delve into the rationale behind the Roman army movements between the northern Duero valley and the southern foothills of the Cantabrian Mountains. We conclude that methodological approaches stimulated by open-access geospatial datasets and enriched by geoscientific techniques are fundamental to understand the expansion of the Roman state in northwestern Iberia during the 1st c. BC properly. This renewed context set up a challenging scenario to overcome traditional archaeological perspectives still influenced by the cultural-historical paradigm and the pre-eminence of classical written sources.
A dense system of pre-Hispanic urban centers has been found in the Upano Valley of Amazonian Ecuador, in the eastern foothills of the Andes. Fieldwork and light detection and ranging (LIDAR) analysis have revealed an anthropized landscape with clusters of monumental platforms, plazas, and streets following a specific pattern intertwined with extensive agricultural drainages and terraces as well as wide straight roads running over great distances. Archaeological excavations date the occupation from around 500 BCE to between 300 and 600 CE. The most notable landscape feature is the complex road system extending over tens of kilometers, connecting the different urban centers, thus creating a regional-scale network. Such extensive early development in the Upper Amazon is comparable to similar Maya urban systems recently highlighted in Mexico and Guatemala.
Airborne light detection and ranging (LiDAR) is a technology that offers the ability to create highly detailed digital terrain models (DTMs) that expose low relief topographic features. The availability of these models holds potential to augment archaeological field research by producing visual imagery that can used to identify traces of ancient anthropogenic activity. This capability is particularly useful in hard to access areas and in areas of dense vegetation, where manual surveys are difficult to plan and to execute. Additionally, LiDAR technology is nonintrusive so that initial surveys can be performed without altering or destroying the integrity of the landscape and any features that it may contain. This paper explores the use of LiDAR within the field of archaeology and uses a case study approach to investigate the potential of LiDAR data for identifying earthworks dating back to the pre-Roman period in central England. Additionally, an evaluation of a technique to enhance the imagery in order to facilitate detecting human activity on the landscape is undertaken. Vegetation cover, particularly during leaf-on periods, can interfere with the ability of LiDAR to penetrate to the surface and can therefore impact its accuracy. The effect of vegetation cover on the ability of LiDAR to produce accurate DTMs is evaluated in relationship to its impact on the identification of archaeological features.
Across the world, cultural heritage is eradicated at an unprecedented rate by development, agriculture, and natural erosion. Remote sensing using airborne and satellite sensors is an essential tool for rapidly investigating human traces over large surfaces of our planet, but even large monumental structures may be visible as only faint indications on the surface. In this paper, we demonstrate the utility of a machine learning approach using airborne laser scanning data to address a “needle-in-a-haystack” problem, which involves the search for remnants of Viking ring fortresses throughout Denmark. First ring detection was applied using the Hough circle transformations and template matching, which detected 202,048 circular features in Denmark. This was reduced to 199 candidate sites by using their geometric properties and the application of machine learning techniques to classify the cultural and topographic context of the features. Two of these near perfectly circular features are convincing candidates for Viking Age fortresses, and two are candidates for either glacial landscape features or simple meteor craters. Ground-truthing revealed the latter sites as ice age features, while the cultural heritage sites Borgø and Trælbanke urge renewed archaeological investigation in the light of our findings. The fact that machine learning identifies compelling new candidate sites for ring fortresses demonstrates the power of the approach. Our automatic approach is applicable worldwide where digital terrain models are available to search for cultural heritage sites, geomorphological features, and meteor impact craters.
Within archaeological prospection, Deep Learning algorithms are developed to detect objects within large remotely sensed datasets. These approaches are generally tested in an (ideal) experimental setting but have not been applied in different contexts or ‘in the wild’, that is, incorporated in archaeological prospection. This research explores the applicability, knowledge discovery—on both a quantitative and qualitative level—and efficiency gain resulting from employing an automated detection tool called WODAN within (Dutch) archaeological practice. WODAN has been used to detect barrows and Celtic fields in LiDAR data from the Dutch Midden-Limburg area, which differs in archaeology, geo-(morpho)logy and land-use from the Veluwe in which it was developed. The results show that WODAN was able to detect potential barrows and Celtic fields, including previously unknown examples, and provided information about the structuring of the landscape in the past. Based on the results, combined human-computer strategies are argued, in which automated detection has a complementary, rather than a substitute role, to manual analysis. This can offset the inherent biases in manual analysis and deal with the problem that current automated detection methods only detect objects similar to the pre-defined target class(es). The incorporation of automated detection into archaeological prospection, in which the results of automated detection are used to highlight areas of interest and to enhance and add detail to existing archaeological predictive maps, seems logical and feasible.
Archaeologists have recently embraced photogrammetry as a low-cost, efficient tool for recording archaeological artifacts, active excavation contexts, and architectural remains. However, no consensus has yet been reached about standard procedures for reliable and metrically accurate photogrammetric recording. The archaeological literature describes diverse equipment and approaches to photogrammetry. The purpose of this article is to open a discussion about when and how photogrammetry should be employed in archaeology in an effort to establish “best practices” for this new method. We focus on the integration of photogrammetry within a comprehensible research plan, the selection of equipment, the appropriate apportionment of labor and time on site, and a rubric for site photography that is conducive to successful and efficient modeling. We conclude that photogrammetric modeling will soon become an indispensable tool in most archaeological applications but should always be implemented in ways that do not place undue burdens on project personnel and budgets and that aid research goals in well-defined ways.
The three-dimensional (3D) revolution promised to transform archaeological practice. Of the technologies that contribute to the proliferation of 3D data, photogrammetry facilitates the rapid and inexpensive digitization of complex subjects in both field and lab settings. It finds additional use as a tool for public outreach, where it engages audiences ranging from source communities to artifact collectors. But what has photogrammetry's function been in advancing archaeological analysis? Drawing on our previous work, we review recent applications to understand the role of photogrammetry for contemporary archaeologists. Although photogrammetry is widely used as a visual aid, its analytical potential remains underdeveloped. Considering various scales of inquiry—graduating from objects to landscapes—we address how the technology fits within and expands existing documentation and data visualization routines, while evaluating the opportunity it presents for addressing archaeological questions and problems in innovative ways. We advance an agenda advocating that archaeologists move from proof-of-concept papers toward greater integration of photogrammetry with research., La revolución tridimensional (3D) prometió transformar la práctica arqueológica. De las tecnologías que han contribuido a la proliferación de datos en 3D, la fotogrametría facilita la digitalización rápida y económica de sujetos complejos tanto en el laboratorio como en el campo. Además, hay utilidad en aplicar la fotogrametría como una técnica de alcance comunitario, donde involucra a audiencias diversas desde comunidades de origen hasta coleccionistas de artefactos. Pero ¿para qué ha servido la fotogrametría en avanzar el análisis arqueológico? Aprovechando estudios anteriores publicados por los autores, revisamos aplicaciones recientes para entender el papel de la fotogrametría para arqueólogos contemporáneos. Aunque ampliamente empleado como ayuda visual, su potencial analítico permanece poco desarollado. Considerando varias escalas de investigación—pasando desde objetos hasta paisajes—examinamos cómo la tecnología encaja dentro y expande métodos existentes de documentación y visualización de datos, mientras evaluamos la oportunidad que presenta en abordar preguntas y problemas arqueológicos en formas innovadoras. Avanzamos una agenda que apoya a que arqueólogos avancen de trabajos de prueba de concepto a una mayor integración con la investigación.
This book explores the cost, expressed in labour, of constructing fortifications during the Late Bronze Age in Greece (ca. 1600 – 1050 BCE). The underlying question for this study is whether the cost of large scale constructions, built with large, unwieldy blocks, may have overstretched the (economic) capabilities of communities, leading to their collapse.
Armed conflicts frequently result in the damage or destruction of archaeological heritage. The occupation by ISIS of parts of Iraq and Syria is no exception. Here, the authors present the results of work focused on Nineveh, as part of a wider research initiative to monitor damage inflicted by ISIS at archaeological sites in northern Iraq. Combining satellite imagery, low-level aerial photography and ground-based reconnaissance, the project presents a condition assessment of Nineveh, as well as a new topographic map of the city. The results demonstrate that a few high-profile acts of deliberate vandalism were accompanied by much more extensive damage caused by construction and rubbish dumping extending across substantial parts of the site.
The recording and 3D modelling of complex archaeological sites is usually associated with high capital and logistical costs, because the data acquisition must be performed by specialists using expensive surveying sensors (i.e., terrestrial laser scanners, robotic total stations and/or ground-penetrating radar). This paper presents a novel, low-cost, user-friendly photogrammetric tool for generating high-resolution and scaled 3D models of complex sites. The results obtained with unmanned aerial vehicle (UAV) photogrammetry of an archaeological site indicate that this approach is semi-automatic, inexpensive and effective, and that it guarantees quality.
This article focuses on the UC San Diego Edom Lowlands Regional Archaeology Project's (ELRAP) development of a Structure from Motion-Based 3D field recording project. It includes a brief discussion of the relative advantages and disadvantages of this technique over other three-dimensional recording systems. The article also describes in detail the equipment and techniques used to collect the data needed to create 3D models of excavation units and sites to help other researchers interested in applying this methodology. We outline the workflow of processing data into 3D datasets and GIS-compatible georeferenced 2D datasets. Finally, there is a discussion of the practicality of adopting this technology for archaeological purposes.
Three-dimensional (3D) modelling of archaeological sites has recently undergone a great development due to the implementation of new acquisition devices and techniques and new processing algorithms. One of the most noteworthy of these is the use of unmanned aerial systems for lifting acquisition devices such as digital cameras to model sites based on photogrammetric techniques. Despite the development and wide use of these systems there are some situations where they are not allowed to fly. In this context we propose a method based on masts which elevate the camera in order to perform photogrammetric surveys of large archeological sites. In this study we analysed the photograph acquisition parameters and the surveying procedure in order to balance in-field the efficiency and the complete coverage of photographs in the study zone, taking into account the requirements of the photogrammetric processing technique to be implemented. The proposed method has been applied to a large and complex archaeological site located in Aswan (Egypt). The results obtained (photogrammetric products and the 3D model) have demonstrated the viability of the method in these cases and the possible implementation of these types of studies for documentation, planning, virtual visualization, etc.
