Detecting Roman land boundaries in aerial photographs using Radon transforms

Bescoby, David

doi:10.1016/j.jas.2005.10.012

Cited by 20 publications

(15 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Automatic and semi‐automatic methods for detection and mapping of archaeological structures from remote sensing data (Table ) have emerged over the last 12 years (see Traviglia, Cowley, & Lambers, , for a discussion of this trajectory). In an early work, Bescoby () used the Radon transform to detect Roman land boundaries from aerial photographs. Template matching has been used to map burials from optical satellite data (Trier et al, ), and to identify a range of objects including pitfall traps, charcoal burning platforms, and grave mounds in a digital terrain model (DTM) derived from ALS (Schneider, Takla, Nicolay, Raab, & Raab, ; Trier & Pilø, ; Trier & Pilø, ; Trier, Pilø, & Johansen, ; Trier, Zortea, & Tonning, ).…”

Section: Introductionmentioning

confidence: 99%

Using deep neural networks on airborne laser scanning data: Results from a case study of semi‐automatic mapping of archaeological topography on Arran, Scotland

Trier

Cowley

Waldeland

2018

Archaeological Prospection

104

View full text Add to dashboard Cite

This article presents results of a case study within a project that seeks to develop heavily automated analysis of digital topographic data to extract archaeological information and to expedite large area mapping. Drawing on developments in computer vision and machine learning, this has the potential to fundamentally recast the capacity of archaeological prospection to cover large areas and deal with mass data, breaking a dependency on human resource. Without such developments, the potential of the vast amount of archaeological information embedded in large topographic and image-based datasets cannot be realized. The purpose of the case study reported on here is to assess existing developments in a Norwegian study against digital topographic data for the island of Arran, Scotland, examining the transferability of the approach and providing a proof of concept in a Scottish context. For Arran, three monument classes were assessedprehistoric roundhouses, shieling huts of medieval or post-medieval date, and small clearance cairns. These present different challenges to detection, with preliminary results ranging from a manageable mix of false positives and true identifications to the chaotic. The influence of variable morphology and the occurrence of other, largely natural, objects of confusion in the landscape is discussed, highlighting the potential improvements in automated detection routines offered by adding anthropogenic and natural false positives to additional confusion classes.

show abstract

Section: Introductionmentioning

confidence: 99%

Using deep neural networks on airborne laser scanning data: Results from a case study of semi‐automatic mapping of archaeological topography on Arran, Scotland

Trier

Cowley

Waldeland

2018

Archaeological Prospection

104

View full text Add to dashboard Cite

show abstract

“…The first archaeological research implementing OBIA was primarily concerned with identifying large‐scale linear features. For example, Bescoby () used a mathematical function known as a Radon transform (which can determine the most common alignment and orientation of features within an image) and segmentation procedures to detect linear Roman structures in satellite imagery. Within a few years, more publications began to emerge using OBIA methods (e.g.…”

Section: Obia and Machine Learning In Archaeologymentioning

confidence: 99%

Object‐based image analysis: a review of developments and future directions of automated feature detection in landscape archaeology

Davis

2018

Archaeological Prospection

View full text Add to dashboard Cite

Object-based image analysis (OBIA) is a method of assessing remote sensing data that uses morphometric and spectral parameters simultaneously to identify features in remote sensing imagery. Over the past 10-15 years, OBIA methods have been introduced to detect archaeological features. Improvements in accuracy have been attained by using a greater number of morphometric variables and multiple scales of analysis. This article highlights the developments that have occurred in the application of OBIA within archaeology and argues that OBIA is both a useful and necessary tool for archaeological research. Additionally, I discuss future research paths using this method. Some of the suggestions put forth here include: pushing for multifaceted research designs utilizing OBIA and manual interpretation, using OBIA methods for directly studying landscape settlement patterns, and increasing data sharing of methods between researchers. KEYWORDS automated feature extraction, landscape analysis, machine learning, object-based image analysis, pattern recognition, remote sensing

show abstract

“…This took five forms. The first involved a study of the aerial photography dating from the Second World War showing two phases of centuriation dating to the Early Roman period (Bescoby 2006; forthcoming) (Figs. 1, 2).…”

Section: Introductionmentioning

confidence: 99%

Beyond Butrint: The ‘Mursi Survey’, 2008

Hodges

Carr

Sebastiani

et al. 2016

Annu. Br. Sch. Athens

View full text Add to dashboard Cite

This article provides a short report on a survey of the region to the east of the ancient city of Butrint, in south-west Albania. Centred on the modern villages of Mursi and Xarra, the field survey provides information on over 80 sites (including standing monuments). Previous surveys close to Butrint have brought to light the impact of Roman Imperial colonisation on its hinterland. This new survey confirms that the density of Imperial Roman sites extends well to the east of Butrint. As in the previous surveys, pre-Roman and post-Roman sites are remarkably scarce. As a result, taking the results of the Butrint Foundation's archaeological excavations in Butrint to show the urban history of the place from the Bronze Age to the Ottoman period, the authors challenge the central theme of urban continuity and impact upon Mediterranean landscapes posited by Horden and Purcell, in The Corrupting Sea (2000). Instead, the hinterland of Butrint, on the evidence of this and previous field surveys, appears to have had intense engagement with the town in the Early Roman period following the creation of the Roman colony. Significant engagement with Butrint continued in Late Antiquity, but subsequently in the Byzantine period, as before the creation of the colony, the relationship between the town and its hinterland was limited and has left a modest impact upon the archaeological record.

show abstract

Detecting Roman land boundaries in aerial photographs using Radon transforms

Cited by 20 publications

References 13 publications

Using deep neural networks on airborne laser scanning data: Results from a case study of semi‐automatic mapping of archaeological topography on Arran, Scotland

Using deep neural networks on airborne laser scanning data: Results from a case study of semi‐automatic mapping of archaeological topography on Arran, Scotland

Object‐based image analysis: a review of developments and future directions of automated feature detection in landscape archaeology

Beyond Butrint: The ‘Mursi Survey’, 2008

Contact Info

Product

Resources

About