Think big about data: Archaeology and the Big Data challenge

Gattiglia, Gabriele

doi:10.11588/ai.2015.1.26155

Cited by 15 publications

(11 citation statements)

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“…This research has shown that the employment of automated detection can lead to both quantitative and qualitative knowledge gain of the archaeological record of a certain region. Undeniably, the ability to rapidly map (multiple classes of) archaeological objects in large remote sensing datasets can radically transform archaeological practice and has broadly positive implications for both research and cultural heritage management (Gattiglia, 2015;Opitz & Herrmann, 2018). Although we should not lose sight of the problems surrounding this shift to a data-intensive approach to science (Huggett, 2020b), from a research standpoint, it offers opportunities for spatial analysis and landscape archaeology .…”

Section: Incorporating Automated Detection Into Archaeological Practicementioning

confidence: 99%

“…Understanding this spatial relation between archaeological objects and their surroundings, that is, the landscape, lies at the core of landscape archaeology (Verhoeven, 2017). The possibility to effectively investigate 'Big' datasets not only means that a phenomenon can be investigated on a wider scale, but also that all available data can be used, instead of a sample, which will let us see details we never could when we were limited to smaller quantities (Gattiglia, 2015).…”

Section: Incorporating Automated Detection Into Archaeological Practicementioning

confidence: 99%

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Applying automated object detection in archaeological practice: A case study from the southern Netherlands

Vaart

Lambers

2021

Archaeological Prospection

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Within archaeological prospection, Deep Learning algorithms are developed to detect objects within large remotely sensed datasets. These approaches are generally tested in an (ideal) experimental setting but have not been applied in different contexts or 'in the wild', that is, incorporated in archaeological prospection. This research explores the applicability, knowledge discovery-on both a quantitative and qualitative level-and efficiency gain resulting from employing an automated detection tool called WODAN within (Dutch) archaeological practice. WODAN has been used to detect barrows and Celtic fields in LiDAR data from the Dutch Midden-Limburg area, which differs in archaeology, geo-(morpho)logy and land-use from the Veluwe in which it was developed. The results show that WODAN was able to detect potential barrows and Celtic fields, including previously unknown examples, and provided information about the structuring of the landscape in the past. Based on the results, combined human-computer strategies are argued, in which automated detection has a complementary, rather than a substitute role, to manual analysis. This can offset the inherent biases in manual analysis and deal with the problem that current automated detection methods only detect objects similar to the pre-defined target class(es). The incorporation of automated detection into archaeological prospection, in which the results of automated detection are used to highlight areas of interest and to enhance and add detail to existing archaeological predictive maps, seems logical and feasible.

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Section: Incorporating Automated Detection Into Archaeological Practicementioning

confidence: 99%

Section: Incorporating Automated Detection Into Archaeological Practicementioning

confidence: 99%

Applying automated object detection in archaeological practice: A case study from the southern Netherlands

Vaart

Lambers

2021

Archaeological Prospection

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“…Especially in Deep Learning, a specialist is needed to correctly create a dataset, select the best CNN architecture, evaluation methods and metrics, and training regime. All those moving parts are very domain dependant, and while there are archaeologists who are familiar with Deep Learning, collaboration between experts from different fields can greatly improve the results of the developed method (see also Gattiglia 2015). The type of collaboration as conducted in this research seems to be especially fruitful, and is able to produce high-quality, high performance models.…”

Section: Cross-domain Collaborationmentioning

confidence: 90%

“…In a more general sense, as archaeology generates more and more data it also necessitates proper data analysis techniques (Huggett 2020;McCoy 2017). With the increased collection, aggregation, and processing of data, the occurrence of errors, inconsistencies, and bias also increases (Gattiglia 2015), which can in turn produce wrong or misleading results. Therefore, a collaboration with computer or data scientists might be of extra benefit to properly catch these errors and biases.…”

Section: Cross-domain Collaborationmentioning

confidence: 99%

Implementing State-of-the-Art Deep Learning Approaches for Archaeological Object Detection in Remotely-Sensed Data: The Results of Cross-Domain Collaboration

Olivier

Vaart

2021

Journal of Computer Applications in Archaeology

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The ever-increasing amount of remotely-sensed data pertaining to archaeology renders human-based analysis unfeasible, especially considering the expert knowledge required to correctly identify structures and objects in these type of data. Therefore, robust and reliable computer-based object detectors are needed, which can deal with the unique challenges of not only remotely-sensed data, but also of the archaeological detection task.In this research -across-domain collaboration between archaeology and computer science -the latest developments in object detection and Deep Learning -for both natural and satellite imagery -are used to develop an object detection approach, based on the YOLOv4 framework, and modified to the specific task of detecting archaeology in remotely-sensed LiDAR data from the Veluwe (the Netherlands). Experiments show that a general version of the YOLOv4 architecture outperforms current object detection workflows used in archaeology, while the modified version of YOLOv4, geared towards the archaeological task, reaches even higher performance. The research shows the potential and benefit of cross-domain collaboration, where expert knowledge from different research fields is used to create a more reliable detector.

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“…Over the past decade, new forms of data such as geospatial data and aerial photography have been included in archaeology research [8], leading to new challenges such as storing massive, heterogeneous data, high-performance data processing and data governance [4]. As a result, archaeologists need a platform that can host, process, analyze and share such data.…”

Section: Introductionmentioning

confidence: 99%

ArchaeoDAL: A Data Lake for Archaeological Data Management and Analytics

Liu

Loudcher

Darmont

et al. 2021

25th International Database Engineering &Amp; Applications Symposium

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With new emerging technologies, such as satellites and drones, archaeologists collect data over large areas. However, it becomes difficult to process such data in time. Archaeological data also have many different formats (images, texts, sensor data) and can be structured, semi-structured and unstructured. Such variety makes data difficult to collect, store, manage, search and analyze effectively. A few approaches have been proposed, but none of them covers the full data lifecycle nor provides an efficient data management system. Hence, we propose the use of a data lake to provide centralized data stores to host heterogeneous data, as well as tools for data quality checking, cleaning, transformation and analysis. In this paper, we propose a generic, flexible and complete data lake architecture. Our metadata management system exploits goldMEDAL, which is the most generic metadata model currently available. Finally, we detail the concrete implementation of this architecture dedicated to an archaeological project. CCS CONCEPTS• Computer systems organization → Data flow architectures; • Information systems → Data warehouses.

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Think big about data: Archaeology and the Big Data challenge

Cited by 15 publications

References 0 publications

Applying automated object detection in archaeological practice: A case study from the southern Netherlands

Applying automated object detection in archaeological practice: A case study from the southern Netherlands

Implementing State-of-the-Art Deep Learning Approaches for Archaeological Object Detection in Remotely-Sensed Data: The Results of Cross-Domain Collaboration

ArchaeoDAL: A Data Lake for Archaeological Data Management and Analytics

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