Making sense of anomalies: Practices and challenges in the archaeological interpretation of geophysical data

Verdonck, Lieven; Smedt, Philippe De; Verhegge, Jeroen

doi:10.1016/b978-0-12-812429-1.00006-4

Cited by 13 publications

(18 citation statements)

References 67 publications

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“…Image pixels are the final output of the geophysical data acquisition and processing chain. Pixel-based methods (Kvamme 2006; Ogden et al 2010; Verdonck et al 2019) have produced significant results, resulting in a clearer depiction of structures that would otherwise be less visible in individual datasets. Nevertheless, several critical issues must be considered with such imagery.…”

Section: Image Processing and Fusionmentioning

confidence: 99%

Ground-penetrating radar survey at Falerii Novi: a new approach to the study of Roman cities

et al. 2020

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Section: Image Processing and Fusionmentioning

confidence: 99%

Ground-penetrating radar survey at Falerii Novi: a new approach to the study of Roman cities

et al. 2020

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“…Despite the great advances in speed and coverage that have come with modern sensor platforms [117][118][119], archaeological geophysics still often has to cope with undersampled data due to the small size of many archaeological features. Hence, it is important that the available data are of the best possible quality as their interpretation can otherwise be very difficult [120]. Many sources of noise cannot be reduced (e.g., inherent electronic instrument noise, soil noise) and it is important to minimize those contributions to noise that can be controlled, for example, due to the careful operation of instruments and best possible positioning.…”

Section: Discussionmentioning

confidence: 99%

Dreaming of Perfect Data: Characterizing Noise in Archaeo-Geophysical Measurements

Schmidt¹,

Dabas

Sarris

2020

Geosciences

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For the interpretation of archaeological geophysical data as archaeological features, it is essential that the recorded anomalies can be clearly delineated and analyzed, and therefore, care has been taken to obtain the best possible data. However, as with all measurements, data are degraded by unwanted components, or noise. This review clarifies the terminology, discusses the four major sources of noise (instrument, use of instrument, external, soil), and demonstrates how it can be characterized using geostatistical and wavenumber methods. It is important to recognize that even with improved instruments, some noise sources, like soil noise, may persist and that degraded data may be the result of unexpected sources, for example, global positioning system synchronization problems. Suggestions for the evaluation and recording of noise levels are provided to allow estimation of the limit of detection for archaeological geophysical anomalies.

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“…Other computer‐assisted detection techniques aside from than the previous ones have been used in archaeological applications of GPR, such as template matching and object‐based image analysis (OBIA) (see, e.g. Verdonck, De Smedt, & Verhegge, 2019). For example, Verdonck (2016) designed a method based on template matching for the extraction of buried linear features from the data and used it to detect Roman wall foundations.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Verdonck (2016) designed a method based on template matching for the extraction of buried linear features from the data and used it to detect Roman wall foundations. Later, Verdonck et al (2019) employed OBIA to extract features corresponding to Roman building foundations from GPR and fluxgate gradiometer data, and features corresponding to pits and ditches from fluxgate gradiometer and earth resistance data. Leckebush et al (2008) developed algorithms for feature detection from GPR data based on 3D regression techniques and applied them to model Roman walls and a water channel, and Linford and Linford (2017) used OBIA to identify circular pit‐type anomalies and linear anomalies associated with Roman structural remains from GPR and magnetic data.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Verdonck (2016) designed a method based on template matching for the extraction of buried linear features from the data and used it to detect Roman wall foundations. Later, Verdonck et al (2019) employed OBIA to extract features corresponding to Roman building foundations from GPR and fluxgate gradiometer data, and features corresponding to pits and ditches from fluxgate gradiometer and earth resistance data. Leckebush et al (2008) In this paper, we present algorithms for automatic detection of reflection patterns of mud walls in radargrams, based on Viola-Jones cascade classifiers (Viola & Jones, 2001, 2004 and the feature descriptors Haar (Viola & Jones, 2001), HOG (Dalal & Triggs, 2005) and LBP (Ojala et al, 1994(Ojala et al, , 2002.…”

Section: Introductionmentioning

confidence: 99%

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Automatic detection of mud‐wall signatures in ground‐penetrating radar data

Bordón

Martinélli

Medina

et al. 2020

Archaeological Prospection

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The ground‐penetrating radar (GPR) method with the standard constant‐offset reflection mode allows to detect and map different types of archaeological structures, such as walls, foundations, floors and roads. The interpretation of the GPR data usually involves a detailed and time‐consuming analysis of large amounts of information, which entails nonnegligible chances of errors, especially under nonideal fieldwork conditions. The application of suitable automatic detection algorithms can be useful to more rapidly and successfully complete the interpretation task. In this work, we explore the use of supervised machine learning methodologies to automatically detect mud‐wall signatures in radargrams and to map the structures from these detections. Several algorithms, based on Viola–Jones cascade classifiers and the image feature descriptors Haar, histogram of oriented gradients and local binary patterns, were implemented. These algorithms were applied to datasets previously acquired in pre‐Inca and Inca‐Hispanic sites located in the Andean NW region of Argentina. The best algorithms provided very good detection rates for well‐preserved walls and acceptable rates for deteriorated walls, with a low number of spurious predictions. They also exhibited the ability to detect collapsed walls and fragments detached from them. These are remarkable results because mud walls are usually difficult to be detected by conventional analysis, owing to the complex and variable characteristics of their reflection patterns. The results of the automatic detection techniques were represented in plan views and three‐dimensional (3D) views that delineated in detail most of the structures of the sites. These algorithms are very fast, so applying them significantly reduces the interpretation times. In addition, once they have been trained using the patterns of one or several sites, they are directly applicable to other sites with similar characteristics.

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Making sense of anomalies: Practices and challenges in the archaeological interpretation of geophysical data

Cited by 13 publications

References 67 publications

Ground-penetrating radar survey at Falerii Novi: a new approach to the study of Roman cities

Ground-penetrating radar survey at Falerii Novi: a new approach to the study of Roman cities

Dreaming of Perfect Data: Characterizing Noise in Archaeo-Geophysical Measurements

Automatic detection of mud‐wall signatures in ground‐penetrating radar data

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