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

Davis, Dylan S.

doi:10.1002/arp.1730

Cited by 79 publications

(98 citation statements)

References 63 publications

(115 reference statements)

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“…A main source of information comes from semi‐automatic detection of barrows, which is an up‐to‐date technique (Cerrillo‐Cuenca, ; Cowley, ; Davis, ; Guyot et al, ; Trier, Zortea, & Tonning, ). Our method is based on a GEOBIA (GEOgraphic‐Object‐Based Image Analysis) approach that segments the DTM raster into regions considering specific properties of the terrain.…”

Section: Methodsmentioning

confidence: 99%

Counting with the invisible record? The role of LiDAR in the interpretation of megalithic landscapes in south‐western Iberia (Extremadura, Alentejo and Beira Baixa)

Cerrillo‐Cuenca¹,

Bueno‐Ramírez

2019

Archaeological Prospection

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The megalithic sites from southwest Iberia represent one of the largest clusters of prehistoric monuments in Europe from the Neolithic and Copper Age (fifth and third millennia cal BC). Unlike other regions from western Europe, there has not been a recent effort to map the distribution of these kinds of burials across this vast territory. Therefore, this article aims to collect geographic information from three regions of southwest Iberia (Alentejo and Beira Baixa from Portugal and Extremadura from Spain) and to compare the archaeological evidence between different landscape units. We have mapped already known megaliths (ca 2000) and settlements (ca 1500) in this area. Moreover, through the interpretation of light detection and ranging (LiDAR) datasets, we have identified new walled enclosures and megaliths in the Extremadura region (Spain), the only one of the three where LiDAR data are available.The new data reveals new connections between settlements, burials and other archaeological evidence. Finally, we discuss the impact that these new data have on a new overall interpretation of megalithic landscapes from the Iberian Peninsula, stressing also the potential risks that the massive application of remote sensing can have in the production of archaeological knowledge.

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Section: Methodsmentioning

confidence: 99%

Counting with the invisible record? The role of LiDAR in the interpretation of megalithic landscapes in south‐western Iberia (Extremadura, Alentejo and Beira Baixa)

Cerrillo‐Cuenca¹,

Bueno‐Ramírez

2019

Archaeological Prospection

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“…Nowadays, RS specialists and archaeologists are giving priority to manual visualization, which is limited to three spectral bands at a time [29]. This means that it is still necessary to intervene manually, which requires a lot of time, manpower and material resources [53][54][55]. With the rapid development of image and signal processing and computer vision, several (semi-) automatic approaches have also been designed for and applied to archaeological research [56][57][58][59][60].…”

Section: Archaeological Remote Sensingmentioning

confidence: 99%

Identifying Linear Traces of the Han Dynasty Great Wall in Dunhuang Using Gaofen-1 Satellite Remote Sensing Imagery and the Hough Transform

et al. 2019

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The Han Dynasty Great Wall (GH), one of the largest and most significant ancient defense projects in the whole of northern China, has been studied increasingly not only because it provides important information about the diplomatic and military strategies of the Han Empire (206 B.C.–220 A.D.), but also because it is considered to be a cultural and national symbol of modern China as well as a valuable archaeological monument. Thus, it is crucial to obtain the spatial pattern and preservation situation of the GH for next-step archaeological analysis and conservation management. Nowadays, remote sensing specialists and archaeologists have given priority to manual visualization and a (semi-) automatic extraction approach is lacking. Based on the very high-resolution (VHR) satellite remote sensing imagery, this paper aims to identify automatically the archaeological features of the GH located in ancient Dunhuang, northwest China. Gaofen-1 (GF-1) data were first processed and enhanced after image correction and mathematical morphology, and the M-statistic was then used to analyze the spectral characteristics of GF-1 multispectral (MS) data. In addition, based on GF-1 panchromatic (PAN) data, an auto-identification method that integrates an improved Otsu segmentation algorithm with a Linear Hough Transform (LHT) is proposed. Finally, by making a comparison with visual extraction results, the proposed method was assessed qualitatively and semi-quantitatively to have an accuracy of 80% for the homogenous background in Dunhuang. These automatic identification results could be used to map and evaluate the preservation state of the GH in Dunhuang. Also, the proposed automatic approach was applied to identify similar linear traces of other generations of the Great Wall of China (Western Xia Dynasty (581 A.D.–618 A.D.) and Ming Dynasty (1368 A.D.–1644 A.D.)) in various geographic regions. Moreover, the results indicate that the computer-based automatic identification has great potential in archaeological research, and the proposed method can be generalized and applied to monitor and evaluate the state of preservation of the Great Wall of China in the future.

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“…Automatic feature detection approaches for archaeology are a rapidly and fast developing field in archaeological prospection [60][61][62]. Recent studies have demonstrated machine learning methods for detecting a wide variety of features, including burial mounds, charcoal kilns, buildings, and field systems [10,[63][64][65][66].…”

Section: Evaluating Our Approachmentioning

confidence: 99%

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

2019

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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 Traelbanke 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.

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

Cited by 79 publications

References 63 publications

Counting with the invisible record? The role of LiDAR in the interpretation of megalithic landscapes in south‐western Iberia (Extremadura, Alentejo and Beira Baixa)

Counting with the invisible record? The role of LiDAR in the interpretation of megalithic landscapes in south‐western Iberia (Extremadura, Alentejo and Beira Baixa)

Identifying Linear Traces of the Han Dynasty Great Wall in Dunhuang Using Gaofen-1 Satellite Remote Sensing Imagery and the Hough Transform

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

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