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Thessaly is a low relief region in Greece where hundreds of Neolithic settlements/tells called magoules were established from the Early Neolithic period until the Bronze Age (6,000 - 3,000 BC). Multi-sensor remote sensing was applied to the study area in order to evaluate its potential to detect Neolithic settlements. Hundreds of sites were geo-ref...
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... band was reclassified in two categories: a) for all pixels within the range of <reflectance>+/-σ and b) for all the pixels outside the specific range. As a result, binary files were created and Boolean addition in GIS environment was followed to produce a final classification map (Figure 7). After the creation of the spectral signature modeling map, 64 settlements in a total of 120 (56.6%) were established in areas of very high possibility. ...
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... The presence of buried archaeological remains influences vegetation and soil, it may modify ground color in the presence of both vegetation and bare soil. The so-called 'crop marks' are useful anomalies that occur because of differential growth of vegetation; in fact, the presence of buried structures may negatively influence the growth of vegetation, while the presence of moats may positively influence it [12][13][14][15]. ...
The Italian territory of Sardinia Island has an enormous cultural and identity heritage from the Pre-Nuragic and Nuragic periods, with archaeological evidence of more than 7000 sites. However, many other undiscovered remnants of these ancient times are believed to be present. In this context, it can be helpful to analyze data from different types of sensors on a single information technology platform, to better identify and perimeter hidden archaeological structures. The main objective of the study is to define a methodology that through the processing, analysis, and comparison of data obtained using different non-invasive survey techniques could help to identify and document archaeological sites not yet or only partially investigated. The non-invasive techniques include satellite, unmanned aerial vehicle, and geophysical surveys that have been applied at the nuraghe Nanni Arrù, one of the most important finds in recent times. The complexity of this ancient megalithic edifice and its surroundings represents an ideal use case. The surveys showed some anomalies in the areas south–east and north–east of the excavated portion of the Nanni Arrù site. The comparison between data obtained with the different survey techniques used in the study suggests that in areas where anomalies have been confirmed by multiple data types, buried structures may be present. To confirm this hypothesis, further studies are believed necessary, for example, additional geophysical surveys in the excavated part of the site.
... 157-158). In this direction, the introduction of digital methods in both field recording and desktop-based registers, including GPS technology, structured open-access databases, 1 GIS-based applications (Bevan & Conolly, 2004;Gillings, 2000) and multi-temporal remote sensing sources (Alexakis et al., 2009;Garcia-Molsosa et al., 2023;Orengo et al., 2015), targets at recovering and storing data no longer visible in the landscape today and, at the same time, complementing this information with new in-field results for a better understanding of the archaeological record. ...
... Historical aerial photographs, nevertheless, have the potential to reveal archaeological imprints nowadays covered by vegetation or destroyed due to land flattening and reclamation processes that took place in Greece between the 1970s and 1990s (Agapiou et al., 2022;Alexakis et al., 2009;Donati & Sarris, 2016;Orengo et al., 2015;Stoker, 2010). In the case of Grevena, the aerial imagery we used Geographical Service (HMGS). ...
Surface archaeological survey has been widely established as the principal method for the regional study of Mediterranean diachronic landscapes. Before the introduction of GPS and digital, GIS‐based recordings in the late 1990s, survey projects employed analogue recording strategies (e.g. personal notebooks, printed forms and cartographic materials) resulting in low‐precision spatial datasets. These archives, termed here as legacy survey data, can today be visualized and analysed using computational tools. The aim of the present work is to exemplify how legacy data can be reused and reproduced to explore unknown aspects of past survey projects. It showcases a multi‐source, GIS‐structured workflow to manage and re‐evaluate data from the region of Grevena, north‐western Greece, where a largely unpublished all‐period extensive survey titled the Grevena Project has pinpointed a rich, yet unavailable to the archaeological community cultural record. The publications lacked critical evaluation of the survey results and significance, such as accurate site locations, size and chronology as well as a description of the field collection strategies used. To recover and combine these data into a single geodataset, a three‐step workflow was created, including the systematic recording of collected artefacts, the deployment of archival and remote‐sensing resources (e.g. georeferenced cartographic and photographic materials and satellite imagery) and the development of a new extensive survey in selected areas for validation purposes. Results indicated heterogeneity in the techniques employed by the Grevena Project for site recognition. They also brought an important assemblage of Palaeolithic finds unrecorded before. Furthermore, large‐scale geomorphological analysis using geomorphometric approaches demonstrated an irregularly high density of sites in elevated areas, which is considered a surveying bias. Remote sensing sources including archival aerial photographs highlighted regional landscape changes (e.g. in forest coverage) revealing architectural remains unmapped before. Finally, the new survey around Ayios Georgios showed the discovery of several new sites, emphasizing a case study of much more complex dynamics than originally considered during the Grevena Project.
... Remote sensing has proved to be a vital instrument for the survey, documentation, and monitoring of archaeological sites [1,2]. It offers a relatively cheap, fast, systematic, and reproducible method of survey, which is capable of documenting and monitoring archaeological sites across huge and/or inaccessible locations within a short period [3][4][5]. The use of remote sensing in archaeology commonly exploits the spectral difference between archaeological features and their surrounding [6,7]. ...
The use of pixel-based remote sensing techniques in archaeology is usually limited by spectral confusion between archaeological material and the surrounding environment because they rely on the spectral contrast between features. To deal with this problem, we investigated the possibility of using geographic object-based image analysis (GEOBIA) to predict archaeological and non-archaeological features. The chosen study area was previously occupied by farming communities and is characterised by natural soils (non-sites), vitrified dung, non-vitrified dung, and savannah woody vegetation. The study uses a three-stage GEOBIA that comprises (1) image object segmentation, (2) feature selection, and (3) object classification. The spectral mean of each band and the area extent of an object were selected as input variables for object classifications in support vector machines (SVM) and random forest (RF) classifiers. The results of this study have shown that GEOBIA approaches have the potential to map archaeological landscapes. The SVM and RF classifiers achieved high classification accuracies of 96.58% and 94.87%, respectively. Visual inspection of the classified images has demonstrated the importance of the aforementioned models in mapping archaeological and non-archaeological features because of their ability to manage the spectral confusion between non-sites and vitrified dung sites. In summary, the results have demonstrated that the GEOBIAs ability to incorporate spatial elements in the classification model ameliorates the chances of distinguishing materials with limited spectral differences.
... Common applications of multispectral optical data in archaeological studies usually involve indices, band ratios, single bands or band combinations that allow for some features to be more easily recognised due to their ability to reflect or absorb radiation at specific wavelengths. More complex analytical approaches may be used, including principal component analysis (PCA) (e.g., Abate and Lasaponara, 2019;Abate et al., 2020;Brandolini et al., 2021), unsupervised (e.g., Valente et al., 2022) and supervised classifications (e.g., Alexakis et al., 2009;Agapiou et al., 2019;Orengo et al., 2020). ...
Satellite remote sensing has become a valuable tool in archaeology, allowing the monitoring of existing and discovery of new sites, and to study their surroundings. In an attempt to identify unknown Late Bronze Age (LBA) archaeological sites in the Serbian Banat region (southern Carpathian Basin), remote sensing techniques for site detection were applied using Sentinel-2 data. A multi-temporal analysis was performed, and the spectral signatures of soil marks from five known LBA settlements were analysed to determine the best conditions for the identification of archaeological features. Several principal component analyses (PCA), band combinations and vegetation indices were calculated. The vegetation indices results from soil marks at known sites demonstrated the impact of settlement characteristics (compositions, subsurface anomalies) on vegetation growth. Applying this further to identify new sites from the satellite data, one hundred and two possible archaeological locations, ranging from only a few hectares to 100 ha, were identified in Banat and Bačka, to the east and west of the Tisza River. Of the sixty-one possible sites identified in Banat, a sample was visited and their chronology confirmed, proving once again the enormous capabilities of Sentinel-2 data analyses for site detection.
... Common applications of multispectral optical data in archaeological studies usually involve indices, band ratios, single bands or band combinations that allow for some features to be more easily recognised due to their ability to reflect or absorb radiation at specific wavelengths. More complex analytical approaches may be used, including principal component analysis (PCA) (e.g., Abate and Lasaponara, 2019;Abate et al., 2020;Brandolini et al., 2021), unsupervised (e.g., Valente et al., 2022) and supervised classifications (e.g., Alexakis et al., 2009;Agapiou et al., 2019;Orengo et al., 2020). ...
Satellite remote sensing has become a valuable tool in archaeology, allowing the monitoring of existing and discovery of new sites, and to study their surroundings. In an attempt to identify unknown Late Bronze Age (LBA) archaeological sites in the Serbian Banat region (southern Carpathian Basin), remote sensing techniques for site detection were applied using Sentinel-2 data. A multi-temporal analysis was performed, and the spectral signatures of soil marks from five known LBA settlements were analysed to determine the best conditions for the identification of archaeological features. Several principal component analyses (PCA), band combinations and vegetation indices were calculated. The vegetation indices results from soil marks at known sites demonstrated the impact of settlement characteristics (compositions, subsurface anomalies) on vegetation growth. Applying this further to identify new sites from the satellite data, one hundred and two possible archaeological locations, ranging from only a few hectares to 100 ha, were identified in Banat and Bačka, to the east and west of the Tisza River. Of the sixty-one possible sites identified in Banat, a sample was visited and their chronology confirmed, proving once again the enormous capabilities of Sentinel-2 data analyses for site detection.
... Since launch, the advances realised by several remote sensors and technologies, such as the potential of providing systematic data over large areas, have led to satellite remote sensing being widely applied to various archaeological studies in several parts of the world [1][2][3][4][5]. ...
... In fact, it represents one of the main centres for copper smelting, manufacturing tools, and various utensils in the region since the beginning of the Second Iron Age (1270-800 BC) [27]. It is characterised by the presence of thousands of archaeological artefacts spread over an area of >1 km 2 ...
In this paper, the feasibility of satellite remote sensing in detecting and predicting locations of buried objects in the archaeological site of Saruq al-Hadid, the United Arab Emirates (UAE) was investigated. Satellite borne Synthetic Aperture Radar (SAR) is proposed as main technology for this initial investigation. In fact, SAR is the only satellite-based technology able to detect buried artefacts from space and it is expected that fine resolution images of ALOS/PALSAR-2 (L-band SAR) would be able to detect large features (> 1 m) that might be buried in subsurface (< 2 m) under optimum conditions i.e., dry, and bare soil. SAR data were complemented with very-high resolution Worldview-3 multispectral images (0.31 m panchromatic, 1.24 m VNIR) to have a vis-ual assessment of the study area and its land cover features. An integrated approach through the application of advanced image processing techniques and geospatial analysis using machine learning was adopted to characterise the site while automating a process and investigate its ap-plicability. Results from SAR feature extraction and geospatial analyses showed detection of the already under excavation areas on the site and predicted new archaeological areas unexplored yet. The validation of these results was performed using previous archaeological works, geolog-ical and geomorphological field surveys. The modelling and prediction accuracies are expected to improve using the insertion of a neural network and backpropagation algorithms based on the performed cluster groups following more recent field surveys. The validated results can provide guidance for future on-site archaeological work. The pilot process developed in this work can, therefore, be applied to similar arid environments for the detection of archaeological features and guidance of on-site investigations
... Numerous wetlands or even dry plains occupied by meadows or forests are often remnants of former lakes (Valsecchi et al. 2006;Michczyńska et al. 2013;Woronko and Pochocka-Szwarc 2013;Kołaczek et al. 2015), which were important water reservoirs. These encouraged and contributed to the long-term occupation of past human settlements (O'Sullivan 1997;Menotti 2004;Alexakis et al. 2009;) by providing a secure water supply, access to food and a natural barrier from enemies (e.g. Zabilska-Kunek, Makowiecki, and Kabaciński 2016;Welc et al. 2018;Mazurkevych et al. 2020). ...
The archaeological microregion in Bruszczewo located along the middle course of the Samica River, was inhabited during the Bronze and Iron Ages by Únětice and Lusatian Urnfields culture people. Earlier literature emphasised that the valley floor was an extensive lake during the Bronze Age. Our research aimed to reconstruct the Bronze and Early Iron Age development of the lake and its transition into peatland. Using environmental archaeology methods, we marked the significant turning points in the lakes history. During the Early Bronze Age, a deep eutrophic lake was present on the valley floor, which supported the defensive nature of the nearby Únětice culture settlement. Around ca. 1880–1690 cal. BC, the lake underwent transgression, forcing people to create a fascine in the shoreline zone of the settlement. Terrestrialization of the lake began ca. 790–540 cal. BC with a gradual overgrowing that ended around the threshold of the eras. When compared to the results from other sites, it seems that these turning points were probably instigated by local triggers rather than global climatic events.
... Globally, satellite data have been widely applied to archaeological site detection (e.g., Agapiou et al., 2013;Alexakis et al., 2009;Altaweel, 2005;De Laet et al., 2007;Keeney & Hickey, 2015;Lasaponara & Masini, 2007, 2011Luo et al., 2019), often in arid regions where many archaeological sites are large and/or highcontrast (e.g., Agapiou et al., 2013;Beck, 2007;Casana, 2020;Hammer et al., 2022). The climate, landscape character and forms of archaeological remains in Scotland, and similar areas of north-west Europe, present a challenge for the use of satellite remote sensing data as components of sites are often small and/or seasonally visible as crop proxies (e.g., Cowley, 2016;Gojda & Hejcman, 2012;Maxwell, 1983;RCAHMS, 1994). ...
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.
... The technology of remote sensing, and especially satellite Earth observation, although not originally designed and established for archaeological purposes, has become a very useful tool in archaeological and cultural heritage (ACH) and is being applied for various uses [47,48]. The existing spaceborne remote sensing tools in ACH generally can be divided into three types, based on the imaging techniques used-multispectral [35,[49][50][51][52][53][54][55][56][57][58], hyperspectral [36][37][38]55,59,60], and synthetic aperture radar (SAR) [61][62][63][64][65][66]. Basic principles, as well as methods that make different remote sensing techniques suitable for ACH and produce some successful results, have been published and pointed out in many recent reviews [47,[67][68][69]. ...
The documentation and protection of archaeological and cultural heritage (ACH) using remote sensing, a non-destructive tool, is increasingly popular for experts around the world, as it allows rapid searching and mapping at multiple scales, rapid analysis of multi-source data sets, and dynamic monitoring of ACH sites and their environments. The exploitation of remote sensing data and their products have seen an increased use in recent years in the fields of archaeological science and cultural heritage. Different spatial and spectral analysis datasets have been applied to distinguish archaeological remains and detect changes in the landscape over time, and, in the last decade, archaeologists have adopted more thoroughly automated object detection approaches for potential sites. These approaches included, among others, object detection methods, such as those of machine learning (ML) and deep learning (DL) algorithms, as well as convolutional neural networks (CNN) and deep learning (DL) models using aerial and satellite images, airborne and spaceborne remote sensing (ASRS), multispectral, hyperspectral images, and active methods (synthetic aperture radar (SAR) and light detection and ranging radar (LiDAR)). Researchers also refer to the potential for archaeologists to explore such artificial intelligence (AI) approaches in various ways, such as identifying archaeological features and classifying them. Here, we present a review study related to the contributions of remote sensing (RS) and artificial intelligence in archaeology. However, a main question remains open in the field of research: the rate of positive contribution of remote sensing and artificial intelligence techniques in archaeological research. The scope of this study is to summarize the state of the art related to AI and RS for archaeological research and provide some further insights into the existing literature.
... In May 2020, open access to the PRISMA system was provided to the scientific, institutional and industrial worldwide community for research and development [21]. Hyperspectral sensors can contribute to assess the chemical-physical composition of the objects to be detected, thanks to their spectral signature [22][23][24]. The sensor has a total of around 240 bands with a spatial resolution of 30 m. ...
Over the last decades, remote sensing techniques have contributed to supporting cultural heritage studies and management, including archaeological sites as well as their territorial context and geographical surroundings. This paper aims to investigate the capabilities and limitations of the new hyperspectral sensor PRISMA (Precursore IperSpettrale della Missione Applicativa) by the Italian Space Agency (ASI), still little applied to archaeological studies. The PRISMA sensor was tested on Italian terrestrial (Alba Fucens, Massa D’Albe, L’Aquila) and marine (Sinuessa, Mondragone, Caserta) archaeological sites. A comparison between PRISMA hyperspectral imagery and the well-known Sentinel-2 Multi-Spectral Instrument (MSI) was performed in order to better understand features and outputs useful to investigate the aforementioned areas. At first, bad bands analysis and noise removal were performed, in order to delete the numerically corrupted bands. Principal component analysis (PCA) was carried out to highlight invisible details in the original image; then, spectral signatures of representative areas were extracted and compared to Sentinel-2 data. At last, a classification analysis (ML and SAM) was performed both on PRISMA and Sentinel-2 imagery. The results showed a full agreement between Sentinel and PRISMA data, enhancing the capability of PRISMA in extrapolating more spectral information and providing a better reliability in the extraction of the features.
