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Searching for Viking Age Fortresses with Automatic Landscape Classification and Feature Detection

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Searching for Viking Age Fortresses with Automatic Landscape Classification and Feature Detection remote sensing Article Searching for Viking Age Fortresses with Automatic Landscape Classification and Feature Detection David Stott 1,2, Søren Munch Kristiansen 2,3,* and Søren Michael Sindbæk 3 1 Department of Archaeological Science and Conservation, Moesgaard Museum, Moesgård Allé 20, 8270 Højbjerg, Denmark 2 Department of Geoscience, Aarhus University, Høegh-Guldbergs Gade 2, 8000 Aarhus C, Denmark 3 Center for Urban Network Evolutions (UrbNet), Aarhus University, Moesgård Allé 20, 8270 Højbjerg, Denmark * Correspondence: [email protected]; Tel.: +45-2338-2424 Received: 19 June 2019; Accepted: 25 July 2019; Published: 12 August 2019 Abstract: Across the world, cultural heritage is eradicated at an unprecedented rate by development, agriculture, and natural erosion. Remote sensing using airborne and satellite sensors is an essential tool for rapidly investigating human traces over large surfaces of our planet, but even large monumental structures may be visible as only faint indications on the surface. In this paper, we demonstrate the utility of a machine learning approach using airborne laser scanning data to address a “needle-in-a-haystack” problem, which involves the search for remnants of Viking ring fortresses throughout Denmark. First ring detection was applied using the Hough circle transformations and template matching, which detected 202,048 circular features in Denmark. This was reduced to 199 candidate sites by using their geometric properties and the application of machine learning techniques to classify the cultural and topographic context of the features. Two of these near perfectly circular features are convincing candidates for Viking Age fortresses, and two are candidates for either glacial landscape features or simple meteor craters. Ground-truthing revealed the latter sites as ice age features, while the cultural heritage sites Borgø and Trælbanke urge renewed archaeological investigation in the light of our findings. The fact that machine learning identifies compelling new candidate sites for ring fortresses demonstrates the power of the approach. Our automatic approach is applicable worldwide where digital terrain models are available to search for cultural heritage sites, geomorphological features, and meteor impact craters. Keywords: airborne laser scanning (ALS); lidar; archaeology; machine learning; feature detection 1. Introduction Finding rare natural or cultural features on the surface of our densely vegetated and fast eroding planet is an urgent challenge. Recently, improvements in sensors, data processing, and visualization have transformed the scope of archaeological discovery and investigation. Successful, recent examples include the use of airborne laser scanning (ALS) data to detect structures at Rome’s ancient harbor [1] or to map the jungle-covered settlement landscape of Cambodia’s Angkor Wat complex [2] and the multi-period city of Jerash, Jordan [3]. In a similar manner, meteor impact craters are detected in Google Earth images [4], or in airborne radar soundings of the Greenland Ice-sheet [5]. Not all archaeological monuments are easily detected. Recently, ALS survey data led to the discovery of a previously unknown Viking Age (10th Century CE) ring fortresses, Borgring, near Copenhagen, Denmark [6]. These rare sites comprise circular turf and timber ramparts 10–18 m broad and up to 5 m high, which enclosed an area of 120 to 250 m in diameter, with a geometrically arranged system of streets and houses in the interior (Figure1). Borgring was overlooked because a Remote Sens. 2019, 11, 1881; doi:10.3390/rs11161881 www.mdpi.com/journal/remotesensing Remote Sens. 2019, 11, x FOR PEER REVIEW 2 of 21 Remoteoverlooked Sens. 2019, 11 because, 1881 a millennium of erosion and truncation by the cultivation mean that only2 of 20subtle remains of the rampart are visible on the surface today as an interrupted bank 0.15 m high. millenniumThe of ring erosion fortresses and truncation are believed by theto form cultivation a networ meank of thatroyal only strongholds subtle remains marking of thea budding rampart state are visibleformation on the during surface the today reign asof anKing interrupted Harald Bluetooth bank 0.15 c. m 958 high. to 986 CE [7]. The regular spacing of the fiveThe known ring fortresses examples are suggest believed that to further form a networkunrecogniz of royaled fortresses strongholds forming marking part of a buddingthis network state may formationhave existed. during Evidence the reign offrom King excavations Harald Bluetooth at Fyrkat c. and 958 Borgring to 986 CE also [7]. suggest The regular that the spacing construction of the of five knownthese fortresses examples may suggest not have that furtherbeen completed unrecognized at th fortressese time of formingtheir abandonment part of this network[8,9]. Thus, may being haveable existed. to confirm Evidence or from refute excavations the existence at Fyrkat of andfurther Borgring examples also suggest is of thatgreat the importance construction for of our theseunderstanding fortresses may of not the have politics been of completed the era. However, at the time to of date, their there abandonment has not been [8,9 a]. systematic Thus, being attempt able to to confirmdiscover or them. refute the existence of further examples is of great importance for our understanding of the politicsLimited of the era.resources However, prohibit to date, manually there has interpreti not beenng a systematic the ALS attemptdata on to a discover national them. scale. We addressedLimited resources this using prohibit an automatic manually feature interpreting detection the workflow ALS data onto look a national for unrecognized scale. We addressed ring fortress 2 this usingin ALS an data automatic covering feature 42,036 detection km of workflow present-day to look Denmark for unrecognized (the whole ring landmass fortress inof ALS the datacountry coveringexcluding 42,036 the km island2 of present-day of Bornholm). Denmark (the whole landmass of the country excluding the island of Bornholm).This presents a difficult problem for feature detection approaches of remote sensing, where a largeThis presentsnumber of a diexamplesfficult problem are usuall fory featureused to detectiontrain classifiers approaches [10–15]. of In remote this case, sensing, these wheremethods a are largeinappropriate, number of examples as Borgring are usually is the only used example to train classifiersof an erosion [10– 15damaged]. In this fortress case, these that methodshas not been are inappropriate,extensively reconstructed as Borgring in is the the recent only example past [16]. of Additionally, an erosion damaged unidentified fortress Trelleborg-type that has not fortresses been extensivelyare unlikely reconstructed to be complete, in the recent as they past may [16 be]. Additionally,truncated by unidentifiedlater development Trelleborg-type and erosion fortresses processes. are unlikelyThus, the to methods be complete, used asin this they research may be must truncated be ca bypable later of developmentdetecting partial and or erosion fragmented processes. features, Thus,which the methods are sensitive used into this features research with must poorly be capable defined of ramparts, detecting partialand not or reliant fragmented on large features, training whichdatasets. are sensitive to features with poorly defined ramparts, and not reliant on large training datasets. To addressTo address this, wethis, used we computer used computer vision methods vision tomethods detect 202,048to detect annular 202,048 topographic annular topographic features in thefeatures ALS data, in the which ALS were data, then which refined were by then training refined a Random by training Forests a Random Classifier Fo onrests the Classifier topographic on the and historicaltopographic context and historical of the known context examples of the (seeknown Figure exam2).ples The classified(see Figure results 2). The were classified further results refined were usingfurther the geometric refined using properties the geometric of the features properties to 199 of candidates. the features We to suggest 199 candidates. that two ofWe the suggest candidates that two shouldof the be furthercandidates investigated should be as further potential investigated ring fortresses as potential of this period.ring fortresses of this period. FigureFigure 1. Confirmed 1. Confirmed and newand new candidates candidates for Viking for Viking Age militaryAge military installations installations and ringand fortressesring fortresses on on the mapthe map of Denmark of Denmark and adjacentand adjacent areas areas of present-day of present-day Germany Germany and Swedenand Sweden showing showing estimates estimates of of relativerelative late Viking late Viking Age humanAge human (10th ( Century10th Century CE) populationCE) population density densit [17],y [17], the confirmed the confirmed Viking Viking Age Age ring fortressesring fortresses (blue-black (blue-black circles), circles) contemporary, contemporary fortified fortified cities, and cities, the royal and centerthe royal in UNESCO center in World UNESCO HeritageWorld site Heritage Jelling (open site Jelling blue) [(open6], the blue) fully developed[6], the fully linear developed defensive linear earthwork defensive across earthwork the southern across the frontier,southern the UNESCO frontier, Worldthe UNESCO Heritage World site Dannevirke, Heritage site and Dannevirke, the two new and candidate the two sites,new candidate Trælbanke sites, and BorgøTrælbanke
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