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Fig. 6. Positive (a) and negative (b) openness calculated for a subset of the ALS area acquired over the grave field Hemlanden at Birka. A large number of burial mounds as well as a part of the linear, NNW-SSE running town rampart can be seen in the image. Positive (c) and negative (d) openness calculated for a subset of the Kreuttal dataset. Multiple paths of varying depths and alignment can be seen, as well as a section of the earthwork bank, ditch and gate which run through the center of the image from NW to SE. In figures (a) and (c), topographically prominent areas and abrupt changes in slope are depicted as lighter in shade, while in figures (b) and (d) the deepest parts of features are displayed in a lighter shade.

Figure 6 Positive (a) and negative (b) openness calculated for a subset of the ALS area acquired over the grave field Hemlanden at Birka. A large number of burial mounds as well as a part of the linear, NNW-SSE running town rampart can be seen in the image. Positive (c) and negative (d) openness calculated for a subset of the Kreuttal dataset. Multiple paths of varying depths and alignment can be seen, as well as a section of the earthwork bank, ditch and gate which run through the center of the image from NW to SE. In figures (a) and (c), topographically prominent areas and abrupt changes in slope are depicted as lighter in shade, while in figures (b) and (d) the deepest parts of features are displayed in a lighter shade.

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Fig. 1. Positive openness (red lines) and negative openness (white lines). Numbers at angles indicate the azimuth of the angle. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) After Yokoyama et al. (2002:259),.
Fig. 1. Positive openness (red lines) and negative openness (white lines). Numbers at angles indicate the azimuth of the angle. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) After Yokoyama et al. (2002:259),.
Fig. 2. Segmentation example: unsatisfactory representation of a feature by under segmentation (left) in comparison with a feature appropriate segmentation (right) In most cases, segments will be smaller than the objects they classify, resulting in more than one segment describing a target feature (over-segmentation), which is due to the relative complexity of real-world features. Therefore, as a final step of OBIA, such segments have to be assigned to a common class. When segments are larger than the real-world objects, the classification process ends up with mixed classes, where each segment describes more than one object (under-segmentation) (Liu and Xia, 2010:189) (Fig. 2). This case should be avoided, since it would result in objects consisting of more than one class, to which the dominant class would be assigned, thus hampering a useful classification. The archaeological site of Birka-Hovgarden, a UNESCO-designated World Cultural Heritage Site located on the neighbouring islands of Bjérk6 and Adels6 in Lake Malaren in Central Sweden, was an important Viking Age settlement and trading place between the years of ca. 750 CE and ca. 950 CE (Fig. 3). With a fortified hill-fort, an earthen town
Fig. 2. Segmentation example: unsatisfactory representation of a feature by under segmentation (left) in comparison with a feature appropriate segmentation (right) In most cases, segments will be smaller than the objects they classify, resulting in more than one segment describing a target feature (over-segmentation), which is due to the relative complexity of real-world features. Therefore, as a final step of OBIA, such segments have to be assigned to a common class. When segments are larger than the real-world objects, the classification process ends up with mixed classes, where each segment describes more than one object (under-segmentation) (Liu and Xia, 2010:189) (Fig. 2). This case should be avoided, since it would result in objects consisting of more than one class, to which the dominant class would be assigned, thus hampering a useful classification. The archaeological site of Birka-Hovgarden, a UNESCO-designated World Cultural Heritage Site located on the neighbouring islands of Bjérk6 and Adels6 in Lake Malaren in Central Sweden, was an important Viking Age settlement and trading place between the years of ca. 750 CE and ca. 950 CE (Fig. 3). With a fortified hill-fort, an earthen town
Fig. 3. Filtered digital terrain model of the northern half of the island of Bjork6 in Lake Malaren. Viking Age Birka was located in the flat fields between the hill-fort, which is visible as a rocky outcrop in the W, surrounded by a rampart in the NW. The grave field Hemlanden, the subject of this study, is located in the north central section of the island. Visualization: semi- transparent sky-view factor calculation over a multidirectional hillshade, calculated using RVT (RVT, 2015). Inset location map source: ESRI. Evidence suggests occupation in the region from at least the Early Neolithic, with one of the largest known Early Neolithic enclosures found at Grossrusbach-Weinsteig (Doneus et al., 2001; Lécker et al., 2009:101), and a series of Middle Neolithic circular ring ditch systems (Kreisgrabenanlage — KGA) identified in both the eastern and western sections of the study area (Locker et al., 2009:103 Neubauer, 2012:154; Neugebauer, 1995:55; Trnka, 1991). Large hill-forts domi- nate the mountainous region in the center of the study area, such as the presumed Bronze Age triple ditch hill-fort of Ochsenberg at the north, and a possible Iron Age Oppidum in the center of the study area. These locations provide both a defensive advantage as well as perfect positions for control over the resources of the region. Numerous other earthworks, particularly conflict remnants from World War II, dot the region and are woven into the fabric of the landscape, superimposed on relict land use from the Bronze Age to the medieval period. For the The Kreuttal case study (LBI, 2015b), is a 56 km? area located north of Vienna, Austria and the subject of an integrated, landscape oriented archaeological prospection project that combines geophysical prospection, remote sensing, survey and excavation methodologies in the search for evidence of past land use, building on decades of
Fig. 3. Filtered digital terrain model of the northern half of the island of Bjork6 in Lake Malaren. Viking Age Birka was located in the flat fields between the hill-fort, which is visible as a rocky outcrop in the W, surrounded by a rampart in the NW. The grave field Hemlanden, the subject of this study, is located in the north central section of the island. Visualization: semi- transparent sky-view factor calculation over a multidirectional hillshade, calculated using RVT (RVT, 2015). Inset location map source: ESRI. Evidence suggests occupation in the region from at least the Early Neolithic, with one of the largest known Early Neolithic enclosures found at Grossrusbach-Weinsteig (Doneus et al., 2001; Lécker et al., 2009:101), and a series of Middle Neolithic circular ring ditch systems (Kreisgrabenanlage — KGA) identified in both the eastern and western sections of the study area (Locker et al., 2009:103 Neubauer, 2012:154; Neugebauer, 1995:55; Trnka, 1991). Large hill-forts domi- nate the mountainous region in the center of the study area, such as the presumed Bronze Age triple ditch hill-fort of Ochsenberg at the north, and a possible Iron Age Oppidum in the center of the study area. These locations provide both a defensive advantage as well as perfect positions for control over the resources of the region. Numerous other earthworks, particularly conflict remnants from World War II, dot the region and are woven into the fabric of the landscape, superimposed on relict land use from the Bronze Age to the medieval period. For the The Kreuttal case study (LBI, 2015b), is a 56 km? area located north of Vienna, Austria and the subject of an integrated, landscape oriented archaeological prospection project that combines geophysical prospection, remote sensing, survey and excavation methodologies in the search for evidence of past land use, building on decades of
Parameters of the ALS data acquisition at Birka-Hovgarden and Kreuttal. Table 1
Parameters of the ALS data acquisition at Birka-Hovgarden and Kreuttal. Table 1
Fig. 4. Filtered DTM showing a central section of the Kreuttal case study area. A large prehistoric earthwork, provisionally dated to the Iron Age, can be seen in the center of the image. The southern portion of the hillfort comprises the extent of the dataset used in this study. Visualization: semi-transparent sky-view factor calculation over a multidirectional hillshade, calculated using RVT (RVT, 2015). Inset location map source: ESRI.
Fig. 4. Filtered DTM showing a central section of the Kreuttal case study area. A large prehistoric earthwork, provisionally dated to the Iron Age, can be seen in the center of the image. The southern portion of the hillfort comprises the extent of the dataset used in this study. Visualization: semi-transparent sky-view factor calculation over a multidirectional hillshade, calculated using RVT (RVT, 2015). Inset location map source: ESRI.
Fig. 5. Data processing workflow for pixel- and object-based semi-automated feature detection strategies employed in this study.
Fig. 5. Data processing workflow for pixel- and object-based semi-automated feature detection strategies employed in this study.
a _ Oe SS _— ee As mentioned above, the object- oriented workflow typically starts with data segmentation and is followed by a classification step. The A wide variety of pixel-based approaches have been developed over the past decades. For this study the fundamental technique, namely a minimum distance classifier (Lillesand and Kiefer, 1999), has been used to classify the archaeological features. In the pixel-based approach applied here, openness is the main variable used for feature definition. In our cases, the addition of DTM-derived calculations such as slope do not significantly increase classification stability. Therefore, in this applica- tion slope is not used, although in other pixel-based workflows this var- iable may be applicable. In order to determine the pixel class label, the Fig. 7. Comparison between Mahalanobis distance (left) and minimum distance (right) classifiers. The classification results using the minimum distance classifier shows a greater number of smaller topographic objects derived from terrain structures classified as archaeological features than the Mahalanobis distance classifier.
a _ Oe SS _— ee As mentioned above, the object- oriented workflow typically starts with data segmentation and is followed by a classification step. The A wide variety of pixel-based approaches have been developed over the past decades. For this study the fundamental technique, namely a minimum distance classifier (Lillesand and Kiefer, 1999), has been used to classify the archaeological features. In the pixel-based approach applied here, openness is the main variable used for feature definition. In our cases, the addition of DTM-derived calculations such as slope do not significantly increase classification stability. Therefore, in this applica- tion slope is not used, although in other pixel-based workflows this var- iable may be applicable. In order to determine the pixel class label, the Fig. 7. Comparison between Mahalanobis distance (left) and minimum distance (right) classifiers. The classification results using the minimum distance classifier shows a greater number of smaller topographic objects derived from terrain structures classified as archaeological features than the Mahalanobis distance classifier.
Fig. 8. Comparison of the manual (left), object-based (middle) and pixel-based (right) classification of the central Hemlanden area at Birka, which contains numerous burial mounds As discussed above, the archaeological context for the Kreuttal dif- ers greatly from that of Birka in both feature density and scale. In the section of the case study chosen to test the classification approaches, eature types include long linear earthworks such as banks, ditches ind path networks, as well as small pits, platforms and irregularly shaped mounds (Fig. 11). In the Kreuttal classification, both approaches dentify features, however even visual inspection shows that the suc- cessful detection rates are relatively low and the misclassification ‘ates are high. The accuracy assessment method used in the Birka exam- dle is not entirely applicable, due in no small part to the increased num- yer of feature classes used in the Kreuttal interpretation. In the lefinition of this scene, the same sort of logic does not exist as it does n the Birka example. Even discounting modern structures or features of no archaeological interest, the feature classes and types are of varying scales and shapes, presenting an altogether more complex scene fot The differences between the two tested classification methods be- come obvious in a more detailed analysis (Fig. 9). In this specific
Fig. 8. Comparison of the manual (left), object-based (middle) and pixel-based (right) classification of the central Hemlanden area at Birka, which contains numerous burial mounds As discussed above, the archaeological context for the Kreuttal dif- ers greatly from that of Birka in both feature density and scale. In the section of the case study chosen to test the classification approaches, eature types include long linear earthworks such as banks, ditches ind path networks, as well as small pits, platforms and irregularly shaped mounds (Fig. 11). In the Kreuttal classification, both approaches dentify features, however even visual inspection shows that the suc- cessful detection rates are relatively low and the misclassification ‘ates are high. The accuracy assessment method used in the Birka exam- dle is not entirely applicable, due in no small part to the increased num- yer of feature classes used in the Kreuttal interpretation. In the lefinition of this scene, the same sort of logic does not exist as it does n the Birka example. Even discounting modern structures or features of no archaeological interest, the feature classes and types are of varying scales and shapes, presenting an altogether more complex scene fot The differences between the two tested classification methods be- come obvious in a more detailed analysis (Fig. 9). In this specific
Accuracy assessment of automatic classified object versus manual classification within the area under consideration at Birka. Table 2
Accuracy assessment of automatic classified object versus manual classification within the area under consideration at Birka. Table 2
Fig. 9. Detail of the classification results; manual (left), object-based (middle), pixel-based (right). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Fig. 9. Detail of the classification results; manual (left), object-based (middle), pixel-based (right). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Accuracy assessment categorized in type 1 and type 2 errors within the area of consider- ation at Birka.
Accuracy assessment categorized in type 1 and type 2 errors within the area of consider- ation at Birka.
Fig. 10. Verification of manual classification results (left) and the object-based classification results (right) with the underlying map of J.J. Nordstrand. Mismatches are partly due to the difficulties of rectification of the historic map.
Fig. 10. Verification of manual classification results (left) and the object-based classification results (right) with the underlying map of J.J. Nordstrand. Mismatches are partly due to the difficulties of rectification of the historic map.
General assessment of automated versus manual feature detection in the Kreuttal case study example. Table 4
General assessment of automated versus manual feature detection in the Kreuttal case study example. Table 4
Fig. 11. Comparison of the manual (a), object-based (b), and pixel-based (c) classification of the Kreuttal dataset. Images (b) and (c) are at a larger scale in order to better depict classification results.
Fig. 11. Comparison of the manual (a), object-based (b), and pixel-based (c) classification of the Kreuttal dataset. Images (b) and (c) are at a larger scale in order to better depict classification results.
Related topics:
Image ProcessingRemote SensingRemote Sensing (Earth Sciences)LiDAR for Landscape ArchaeologySemi-Automatic ClassificationLiDAR for ArchaeologyOBIA (Object Based Image Analysis)
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