Scratching the surface of war. Airborne laser scans of the Great War conflict landscape in Flanders (Belgium)

Gheyle, Wouter; Stichelbaut, Birger; Saey, Timothy; Berghe, Hanne Van den; Eetvelde, Veerle Van; Meirvenne, Marc Van; Bourgeois, Jean

doi:10.1016/j.apgeog.2017.11.011

Cited by 41 publications

(32 citation statements)

References 29 publications

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“…Stichelbaut (2009) analyzed the shape variability of the trenches he observed in Flanders but did not classify shelters because of their low visibility on historical aerial photographs. Gheyle et al (2018) completed Stichelbaut's typology through the use of LiDAR in the same area, suggesting different shelter geometries. Devos et al (2015) and Taborelli et al (2017b) also focused on trench geometries near Reims and in the Argonne Region and proposed a general polemoform typology without giving morphometric information.…”

Section: An Original Polemoform Typologymentioning

confidence: 98%

“…Kobiałka et al (2016) performed a LiDAR analysis of polemoforms induced by the 1939 fighting along the Brda River (Poland). Finally, Gheyle et al (2018) proposed a LiDAR analysis of WWI polemoforms near Ypres (Belgium). Van der Schriek and Beex (2017) published another related work where they identified WWII remnants near the town of Bussum (Netherlands) by means of LiDAR.…”

Section: Introductionmentioning

confidence: 99%

“…Van der Schriek and Beex (2017) published another related work where they identified WWII remnants near the town of Bussum (Netherlands) by means of LiDAR. Finally, Gheyle et al (2018) proposed a LiDAR analysis of WWI polemoforms near Ypres (Belgium).…”

Section: Introductionmentioning

confidence: 99%

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War landform mapping and classification on the Verdun battlefield (France) using airborne LiDAR and multivariate analysis

Matos-Machado

Toumazet

Bergès

et al. 2019

Earth Surf Processes Landf

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Acting as efficient earth‐movers, soldiers can be viewed as significant geomorphological drivers of landscape change when replaced in the recent debates on Anthropocene Geomorphology. ‘Polemoforms’, generated by military activities, correspond with a set of human‐made landforms of various sizes and geometries. They are particularly common on the World War One battlefield of Verdun (France) which ranks among the largest battles of attrition along the Western Front. The artillery bombardments and building of defensive positions in that battle significantly altered the landscape, resulting in thousands of shell craters, dugouts, and gun positions that have altered both the meso and microtopography. This paper proposes an innovative methodology to make an exhaustive inventory of these small‐scale conflict‐induced landforms (excluding linear features such as trenches) using a digital terrain model (DTM) acquired by airborne LiDAR on the whole battlefield. Morphometric analysis was conducted using Kohonen's self‐organizing maps (SOMs) and hierarchical agglomerative clustering (HAC) in order to quantify and classify the high number of war landforms. This combined approach allowed for mapping more than one million landforms which can be classified into eight different shapes including shell craters and various soldier‐made landforms (i.e. shelters, gun positions, etc.). Detection quality evaluation using field observations revealed the algorithm successfully classified 93% of shell craters and 74% of anthropologically constructed landforms. Finally, the iconographic database and map series produced will help archaeologists and foresters to better manage the historic site of Verdun, today covered by a large forest of ~10 000 ha. © 2019 John Wiley & Sons, Ltd.

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Section: An Original Polemoform Typologymentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

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War landform mapping and classification on the Verdun battlefield (France) using airborne LiDAR and multivariate analysis

Matos-Machado

Toumazet

Bergès

et al. 2019

Earth Surf Processes Landf

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“…Regardless, the infancy of the method is not a reason to stop developing and improving its ability to discern information of archaeological significance. OBIA and similar methods are imperfect and cannot replace manual evaluation completely, but at the same time, biases in knowledge by data analysts limit the accuracy of manual procedures and can lead to omission error (Bennett et al, ; Gheyle et al, ). It can never be our goal to completely automate the archaeological process, and to attempt such a feat would be a fool's errand.…”

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

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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

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“…One aspect is being tackled by Ghent University's multidisciplinary research project “Non‐Invasive Landscape Archaeology of the Great War” (2014–2018), which focuses on identifying war heritage still buried in the Belgian WW1 battlefields without digging into the ground. Historical aerial photographs (Stichelbaut, , ), Lidar (Gheyle et al, ; Stichelbaut et al, ), and landscape changes (Gheyle, Dossche, Bourgeois, Stichelbaut, & Van Eetvelde, ; Van den Berghe et al, ) are being studied as well as geophysical measurements (Saey, Gheyle et al, ; Saey, Note et al, ) which have been introduced to map the former and current surface changes in soil parameters and the buried heritage related to WW1 activities. Using a set of popular geophysical prospecting techniques, we are trying to evaluate the possibilities of applying these sensors to prospect noninvasively the WW1 battlefields in Belgium.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of fluxgate magnetometry and electromagnetic induction surveys for subsurface characterization of archaeological features in World War 1 battlefields

et al. 2018

Self Cite

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Geophysical prospection as a noninvasive archaeological survey technique has become a widely applied discipline that is currently finding its way into the former World War 1 (WW1) battlefields. Because of the imminent danger of unexploded ordnances, noninvasiveness is the key to exploring the subsurface containing our buried heritage. Fluxgate magnetometry (FGM), a frequently applied prospection technique in archaeology and unexploded ordnance detection, is not always satisfactory in this area. This is due to the high density of ferrous objects masking the underlying features of interest. Frequency domain electromagnetic induction (EMI) has proved to be less sensitive to metal objects, thereby revealing more WW1 traces. Another advantage of EMI is that soil disturbances related to shelling and metal objects can be classified with higher precision by combining magnetic and electrical conductivity data layers. After evaluating both prospecting techniques on maximum feature coverage, feature type identification, metal object filtering, and localization, EMI was shown to be the most successful. Although FGM is not inferior (providing high survey speed and density with multisensor arrays and reliability in pinpointing features, etc.) overall, EMI is the preferred option on sites with heavily disturbed soil. In this paper, these findings will be demonstrated with survey examples, case studies, and numerical analyses from the WWI battlefields of Flanders.

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Scratching the surface of war. Airborne laser scans of the Great War conflict landscape in Flanders (Belgium)

Cited by 41 publications

References 29 publications

War landform mapping and classification on the Verdun battlefield (France) using airborne LiDAR and multivariate analysis

War landform mapping and classification on the Verdun battlefield (France) using airborne LiDAR and multivariate analysis

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

Evaluation of fluxgate magnetometry and electromagnetic induction surveys for subsurface characterization of archaeological features in World War 1 battlefields

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