Finding Vanished Routes: Applying a Multi-modelling Approach on Lost
                        Route and Path Networks in the Veluwe Region, the Netherlands

Vletter, Willem F.; Lanen, Rowin J. van

doi:10.16993/rl.35

Cited by 12 publications

(18 citation statements)

References 12 publications

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“…Nowadays, this area, approximately 2200 km 2 (circa 5% of the total area of the Netherlands), is predominantly covered by forest and heath, interspersed with agricultural fields and areas of habitation of various size (for a detailed overview of the research area, see [9,22]). The Veluwe holds one of the densest concentrations of known archaeological objects in the Low Countries, including prehistoric barrows [26] and Celtic fields [27], (post)medieval charcoal kilns [28], hollow roads [29,30], iron extraction pits, and landweren (border barriers), as well as more recent traces of conflict such as fortifications, military (support) structures, and bomb craters [31].…”

Section: Research Areamentioning

confidence: 99%

Combining Deep Learning and Location-Based Ranking for Large-Scale Archaeological Prospection of LiDAR Data from The Netherlands

Vaart

Lambers

Kowalczyk

et al. 2020

IJGI

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This paper presents WODAN2.0, a workflow using Deep Learning for the automated detection of multiple archaeological object classes in LiDAR data from the Netherlands. WODAN2.0 is developed to rapidly and systematically map archaeology in large and complex datasets. To investigate its practical value, a large, random test dataset—next to a small, non-random dataset—was developed, which better represents the real-world situation of scarce archaeological objects in different types of complex terrain. To reduce the number of false positives caused by specific regions in the research area, a novel approach has been developed and implemented called Location-Based Ranking. Experiments show that WODAN2.0 has a performance of circa 70% for barrows and Celtic fields on the small, non-random testing dataset, while the performance on the large, random testing dataset is lower: circa 50% for barrows, circa 46% for Celtic fields, and circa 18% for charcoal kilns. The results show that the introduction of Location-Based Ranking and bagging leads to an improvement in performance varying between 17% and 35%. However, WODAN2.0 does not reach or exceed general human performance, when compared to the results of a citizen science project conducted in the same research area.

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Section: Research Areamentioning

confidence: 99%

Combining Deep Learning and Location-Based Ranking for Large-Scale Archaeological Prospection of LiDAR Data from The Netherlands

Vaart

Lambers

Kowalczyk

et al. 2020

IJGI

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“…Recent research-driven excavations on the Veluwe have mainly focused on barrows (newly discovered in remotely sensed data) and their immediate surrounding landscape [64][65][66]. Recent large-scale surveys include the study of Celtic fields by Arnoldussen [67], the study of hollow ways and roads by Vletter and Van Lanen [68], and the research on barrow landscapes by Bourgeois [69]. The general research strategy in this area (and The Netherlands) consists of a stepped system of: 1) A desktop-survey; followed by 2) a field survey; and finally, 3) a (minimal) invasive survey, i.e., hand corings, test trenches, and excavations ( Figure 3; see also Reference [70]).…”

Section: The Research Area and Current Research Strategymentioning

confidence: 99%

“…Archaeological objects, discernable in the LiDAR data, were compared with the locations of known archaeological objects [50]. The (geo)information of these known archaeological objects was derived from a multitude of databases, including the Dutch national archaeological database ArchIS [78], the Dutch archaeological monument registry AMR [79], and the results of different large-scale surveys of the research area [67][68][69]. Sub-images containing known archaeological objects were labeled and the necessary metadata was created in order to use the images for our deep learning approach.…”

Section: Multi-class Object Detection In Remotely Sensed Data Using Dmentioning

confidence: 99%

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Integrating Remote Sensing, Machine Learning, and Citizen Science in Dutch Archaeological Prospection

2019

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Although the history of automated archaeological object detection in remotely sensed data is short, progress and emerging trends are evident. Among them, the shift from rule-based approaches towards machine learning methods is, at the moment, the cause for high expectations, even though basic problems, such as the lack of suitable archaeological training data are only beginning to be addressed. In a case study in the central Netherlands, we are currently developing novel methods for multi-class archaeological object detection in LiDAR data based on convolutional neural networks (CNNs). This research is embedded in a long-term investigation of the prehistoric landscape of our study region. We here present an innovative integrated workflow that combines machine learning approaches to automated object detection in remotely sensed data with a two-tier citizen science project that allows us to generate and validate detections of hitherto unknown archaeological objects, thereby contributing to the creation of reliable, labeled archaeological training datasets. We motivate our methodological choices in the light of current trends in archaeological prospection, remote sensing, machine learning, and citizen science, and present the first results of the implementation of the workflow in our research area.

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“…The dating and function of physical pathways can only be ascertained reliably through a process of interventions on the ground that extract dateable materials and integrate other sources carrying chronological information. Comparative historical mapping (Vletter & van Lanen 2018), aerial photography (Verbrugghe, De Clercq & Van Eetvelde 2017) and archaeological survey data (Fruchart 2016;Crozet, Laplaige & Rodier 2017) are all used as additional data sets to establish a chronology for the observed features, but this remains challenging.…”

Section: Characterizing the Observational Datamentioning

confidence: 99%

Developing FAIR Ontological Pathways: Linking Evidence of Movement in Lidar to Models of Human Behaviour

Nuninger

Opitz

Verhagen

et al. 2020

Journal of Computer Applications in Archaeology

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This paper proposes an ontological approach to connect the archaeological topographic evidence for movement in the landscape which can be derived from interpretation and spatial analysis of airborne lidar data with models of movement derived from modeling exercises such as Agent Based Modelling or Cost Path Modelling. This computational ontology enables the investigation of movement and its topographic manifestations in the landscape at various spatio-temporal scales. It creates an explicit framework for accessing meaningful information about movement generated through research using both detection and modelling-led approaches. Developing explicit computational frameworks to provide meaningful context is critical, particularly as remote sensing and modelling projects increase in scale and complexity. The process of developing a computational ontology exposes a deeper underlying issue, and one applicable to many topics we address as archaeologists: if we begin to unpack the concept of 'movement' it is readily apparent that it is a complex phenomenon, like many human habits, and studying it requires drawing together a variety of types of physical evidence and multiple, often competing, theoretical models of human processes and practices. If we wish to make archaeological 'data' on movement available, how do we create appropriate contextual information -really useful metadata -so that this data can be incorporated into the variety of studies for which knowledge of movement is relevant? This is essentially the challenge posed broadly by the FAIR principles, and in particular by the principle of interoperability, which suggests that we "use a formal, accessible, shared, and broadly applicable language for knowledge representation". Rather than simply seeking to fulfill the requirements of an arbitrary standard, attempting to meet the challenge of interoperability provides an impetus and opportunity to attempt to bridge the gap between data and model, and to reconsider how we conceive and represent knowledge in archaeological digital data and modelling projects. This kind of computational ontology, we suggest, can serve as the key for making the data from both these sources actually FAIR.

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Finding Vanished Routes: Applying a Multi-modelling Approach on Lost Route and Path Networks in the Veluwe Region, the Netherlands

Cited by 12 publications

References 12 publications

Combining Deep Learning and Location-Based Ranking for Large-Scale Archaeological Prospection of LiDAR Data from The Netherlands

Combining Deep Learning and Location-Based Ranking for Large-Scale Archaeological Prospection of LiDAR Data from The Netherlands

Integrating Remote Sensing, Machine Learning, and Citizen Science in Dutch Archaeological Prospection

Developing FAIR Ontological Pathways: Linking Evidence of Movement in Lidar to Models of Human Behaviour

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