Reconstruction of a Mediterranean coast archaeological site by integration of geophysical and archaeological data: The eastern district of the ancient city of Nora (Sardinia, Italy)

Maio, Rosa Di; Manna, Mauro La; Piegari, E.; Zara, Arturo; Bonetto, Jacopo

doi:10.1016/j.jasrep.2018.05.003

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…However, the overall multi-temporal and multi-scale results were only visually cross-compared with each other. Moreover, data integration is often restricted to similar nature of data (e.g., data obtained from satellite sensors such as pan-sharpening techniques [12][13][14][15], data enhancement of geophysical prospection data [16][17][18][19], etc. ).…”

Section: Introductionmentioning

confidence: 99%

Beyond GIS Layering: Challenging the (Re)use and Fusion of Archaeological Prospection Data Based on Bayesian Neural Networks (BNN)

Αγαπίου

Sarris

2018

Remote Sensing

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Multisource remote sensing data acquisition has been increased in the last years due to technological improvements and decreased acquisition cost of remotely sensed data and products. This study attempts to fuse different types of prospection data acquired from dissimilar remote sensors and explores new ways of interpreting remote sensing data obtained from archaeological sites. Combination and fusion of complementary sensory data does not only increase the detection accuracy but it also increases the overall performance in respect to recall and precision. Moving beyond the discussion and concerns related to fusion and integration of multisource prospection data, this study argues their potential (re)use based on Bayesian Neural Network (BNN) fusion models. The archaeological site of Vésztő-Mágor Tell in the eastern part of Hungary was selected as a case study, since ground penetrating radar (GPR) and ground spectral signatures have been collected in the past. GPR 20 cm depth slices results were correlated with spectroradiometric datasets based on neural network models. The results showed that the BNN models provide a global correlation coefficient of up to 73%—between the GPR and the spectroradiometric data—for all depth slices. This could eventually lead to the potential re-use of archived geo-prospection datasets with optical earth observation datasets. A discussion regarding the potential limitations and challenges of this approach is also included in the paper.

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Section: Introductionmentioning

confidence: 99%

Beyond GIS Layering: Challenging the (Re)use and Fusion of Archaeological Prospection Data Based on Bayesian Neural Networks (BNN)

Αγαπίου

Sarris

2018

Remote Sensing

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“…The received signal consists in two components: in‐phase and out‐of‐phase with the transmitted signal. These components are (under low induction number) related to apparent magnetic susceptibility and bulk apparent resistivity (e.g., Di Maio et al, 2018; Tabbagh, 2017). FDEM surveys are usually used in combination with other methods for identification of building foundations (e.g., Di Maio et al, 2018) and ditches (e.g., Simpson et al, 2010) or for archaeological site prospection generally (e.g., de Smedt et al, 2013; Rodrigues et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

“…These components are (under low induction number) related to apparent magnetic susceptibility and bulk apparent resistivity (e.g., Di Maio et al, 2018; Tabbagh, 2017). FDEM surveys are usually used in combination with other methods for identification of building foundations (e.g., Di Maio et al, 2018) and ditches (e.g., Simpson et al, 2010) or for archaeological site prospection generally (e.g., de Smedt et al, 2013; Rodrigues et al, 2009). FDEM survey offers a combination of both magnetic and resistivity information, but, due to its dependency on the principles of EM induction, it cannot be used in the vicinity of such human‐made conductive structures as pipelines or electric fences.…”

Section: Introductionmentioning

confidence: 99%

Capabilities and limitations of electrical resistivity tomography for mapping and surveying hillfort fortifications

Klanica

Křivánek

Grison

et al. 2022

Archaeological Prospection

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Hillforts are fortified archaeological sites built from the Neolithic to Early Middle Ages within the area of Europe. They were usually surrounded by fortifications consisting of various combinations of ramparts and ditches, which today constitute their most striking remains. Although magnetometry surveys are commonly used for spatial identification of ramparts and ditches, a different method must be employed for directly obtaining depth information. Hence, we evaluate the potential of electrical resistivity tomography (ERT) for surveying hillfort fortifications. Within three hillforts of different ages in the Czech Republic, we investigated various features affecting the imaging ability of ERT, including bedrock type, building material, present‐day condition of fortification, impact of past or recent agricultural activities, and field settings of the ERT method. Supported by additional information from magnetometry and electromagnetic surveys, the results show that ERT is most applicable in cases of stony ramparts, ditches carved into rocky bedrock or well‐preserved earthen ramparts. Poorer results were achieved upon active and/or recently active agricultural lands, where fortifications have been gradually destroyed by ploughing. The remains of stony ramparts remained distinguishable in the latter case, but mere traces of earthen ramparts and ditches were invisible to ERT due to mixing of fortification material with on‐site soil. ERT is a unique method for detailed investigation of both ramparts and ditches by which a structure and its extent can be evaluated to indicate the function of a settlement and obtain information about former environmental conditions, population, land use and/or human–environmental interaction.

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Magnetic Data Profiles Interpretation for Mineralized Buried Structures Identification Applying the Variance Analysis Method

Essa

Mehanee

Elhussein

2020

Pure Appl. Geophys.

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Reconstruction of a Mediterranean coast archaeological site by integration of geophysical and archaeological data: The eastern district of the ancient city of Nora (Sardinia, Italy)

Cited by 4 publications

References 13 publications

Beyond GIS Layering: Challenging the (Re)use and Fusion of Archaeological Prospection Data Based on Bayesian Neural Networks (BNN)

Beyond GIS Layering: Challenging the (Re)use and Fusion of Archaeological Prospection Data Based on Bayesian Neural Networks (BNN)

Capabilities and limitations of electrical resistivity tomography for mapping and surveying hillfort fortifications

Magnetic Data Profiles Interpretation for Mineralized Buried Structures Identification Applying the Variance Analysis Method

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