Papers by Wouter Verschoof-van der Vaart
Antiquity, 2024
Volunteers are a key part of the archaeological labour force and, with the growth of digital data... more Volunteers are a key part of the archaeological labour force and, with the growth of digital datasets, these citizen scientists represent a vast pool of interpretive potential; yet, concerns remain about the quality and reliability of crowd-sourced data. This article evaluates the classification of prehistoric barrows on lidar images of the central Netherlands by thousands of volunteers on the Heritage Quest project. In analysing inter-user agreement and assessing results against fieldwork at 380 locations, the authors show that the probability of an accurate barrow identification is related to volunteer consensus in image classifications. Even messy data can lead to the discovery of many previously undetected prehistoric burial mounds.
Antiquity, 2024
Volunteers are a key part of the archaeological labour force and, with the growth of digital data... more Volunteers are a key part of the archaeological labour force and, with the growth of digital datasets, these citizen scientists represent a vast pool of interpretive potential; yet, concerns remain about the quality and reliability of crowd-sourced data. This article evaluates the classification of prehistoric barrows on lidar images of the central Netherlands by thousands of volunteers on the Heritage Quest project. In analysing inter-user agreement and assessing results against fieldwork at 380 locations, the authors show that the probability of an accurate barrow identification is related to volunteer consensus in image classifications. Even messy data can lead to the discovery of many previously undetected prehistoric burial mounds.
CAA 2021 – “Digital Crossroads”, 2021
Nowadays, archaeologists have vast amounts of Light Detection and Ranging (LiDAR) and other remot... more Nowadays, archaeologists have vast amounts of Light Detection and Ranging (LiDAR) and other remote sensing data at their disposal, to search for previously undiscovered archaeological objects, often at a national scale. This leads to a Big Data problem in archaeology: some degree of automation is needed, as humans alone cannot cope with these ever-growing data sources. In this research, we have developed a novel workflow based on the Artificial Intelligence (AI) technology of Convolutional Neural Networks (CNNs), to automate the detection of unknown, complex archaeological objects. Our hypothesis is that a high-quality remote sensing data source such as LiDAR and a curated list of known objects, is sufficient to find a large number-or ideally all-additional undiscovered objects within a landscape. In a case study presented here, we use Prehistoric hillforts in England as an example for this workflow and present a three-step approach to demonstrate its efficiency.
Quaternary geochronology, Jun 1, 2024
Archaeopress Publishing Ltd eBooks, Sep 2, 2019
Journal of Field Archaeology
Journal of computer applications in archaeology, 2019
Introduction Generally, the data from remote sensing surveys is screened manually in archaeology.... more Introduction Generally, the data from remote sensing surveys is screened manually in archaeology. However, constant monitoring of the earth's surface-by a multitude of airborne and satellite sensors-causes a huge influx of data of high complexity and high quality. To cope with this ever-growing set of largely digital and easily available data, computer-aided methods for the processing of data and the detection of archaeological objects 1 are needed (Bennett, Cowley & De Laet 2014: 896). Over a decade ago, archaeologists started developing computational methods for the (semi-)automated detection of archaeological objects (De Boer 2007; De Laet, Paulissen & Waelkens 2007). Since then multiple case studies have shown these algorithms to be capable of detecting well-defined archaeological traces, such as barrows (see for example Sevara et al. 2016). However, these (often) handcrafted algorithms are highly specialised on specific, single object categories and data sources, which restricts their use in different contexts and limits their usability in general for archaeological prospection. Furthermore, these approaches are predominantly complex algorithms that can require a high level of expertise to operate, and are regularly dependent on expensive software. All this results in an implementation that is limited in its user-friendliness (see also Ball, Anderson & Seng Chan 2017: 3). To overcome the aforementioned limitations, this research project explores the implementation of advanced computational methods to develop a generic, flexible and robust automated detection method for archaeological objects in remotely sensed data. More specifically, this project aims to develop user-friendly workflows for the detection of multiple classes of archaeological objects in LiDAR (Light Detection And Ranging; Wehr & Lohr 1999) data using Deep Learning (Goodfellow, Bengio & Courville 2016). The research project, a four-year PhD, is part of the Data Science Research Programme (DSRP) at the Faculty of Archaeology and the Leiden Centre of Data Science (LCDS) at Leiden University. The DSRP aims to bring together domain knowledge and associated 'big data' problems (for instance in archaeology) with the technical methods and solutions from data science. This paper presents the results of the first year of the PhD project consisting of the first workflow developed, called WODAN (Workflow for Object Detection of Archaeology in the Netherlands). WODAN has successfully been implemented on LiDAR data from the research area in the Netherlands (Figure 1). The workflow serves as a proof of concept, to demonstrate that by implementing deep learning techniques it is possible to create a multi-class
IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium
Journal of Field Archaeology
Archaeological Prospection
In the past decade, numerous studies have successfully mapped thousands of former charcoal produc... more In the past decade, numerous studies have successfully mapped thousands of former charcoal production sites (also called relict charcoal hearths) manually using digital elevation model (DEM) data from various forested areas in Europe and the northeastern USA. The presence of these sites causes significant changes in the soil physical and chemical properties, referred to as legacy effects, due to high amounts of charcoal that remain in the soils. The overwhelming amount of charcoal hearths found in landscapes necessitates the use of automated methods to map and analyse these landforms. We present a novel approach based on open source data and software, to automatically detect relict charcoal hearths in large-scale LiDAR datasets (visualized with Simple Local Relief Model). In addition, the approach simultaneously provides both general as well as domain-specific information, which can be used to further study legacy effects. Different versions of the methodology were fine-tuned on data from northwestern Connecticut and subsequently tested on two different areas in Connecticut. The results show that these perform adequate, with F1-scores ranging between 0.21 and 0.76, although additional post-processing was needed to deal with variations in LiDAR quality. After testing, the best performing version of the prediction model (with an average F1-score of 0.56) was applied on the entire state of Connecticut. The results show a clear overlap with the known distribution of charcoal hearths in the state, while new concentrations were found as well. This shows the usability of the approach on large-scale datasets, even when the terrain and LiDAR quality varies.
Archaeological Prospection, 2022
This paper discusses how the use of AI (artificial intelligence) detected later prehistoric field... more This paper discusses how the use of AI (artificial intelligence) detected later prehistoric field systems provides a more reliable base for reconstructing palaeodemographic trends, using the Netherlands as a case study. Despite its long tradition of settlement excavations, models that could be used to reconstruct (changes in) prehistoric land use have been few and often relied on (insufficiently mapped) nodal data points such as settlements and barrows. We argue that prehistoric field systems of field plots beset on all sides by earthen banks—known as Celtic fields—are a more suitable (i.e. less nodal) proxy for reconstructing later prehistoric land use.
For four 32.25 km2 case study areas in different geogenetic regions of the Netherlands, prehistoric land use surface areas are modelled based on conventional methods and the results are compared to the results we obtained by using AI-assisted detection of prehistoric field systems. The nationally available LiDAR data were used for automated detection. Geotiff DTM images were fed into an object detection algorithm (based on the YOLOv4 framework and trained with known Dutch sites), and resultant geospatial vectors were imported into GIS.
Our analysis shows that AI-assisted detection of prehistoric embanked field systems on average leads to a factor 1.84 increase in known surface areas of Celtic fields. Modelling the numbers of occupants from this spatial coverage, yields population sizes of 37–135 persons for the case study regions (i.e. 1.15 to 4.19 p/km2). This range aligns well with previous estimates and offers a more robust and representative proxy for palaeodemographic reconstructions. Variations in land use coverage between the regions could be explained by differences in present-day land use and research intensity. Particularly the regionally different extent of forestlands and heathlands (ideal for the (a) preservation and (b) automated LiDAR detection of embanked field systems) explains minor variations between the four case study regions.
Remote Sensing, Mar 31, 2022
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
<p>The mining region of Upper Silesia has a long tradition with international signi... more <p>The mining region of Upper Silesia has a long tradition with international significance. In 2017, the historic silver mine in Tarnowsky Gory was recognized as a UNESCO World Heritage Site. With the mining of galena (PbS), the region developed into one of the most important industrial centers in Central Europe in the 16th century. In addition to the underground galleries, the historical mining has left thousands of mining shafts as small relief forms, which have not been systematically investigated so far. Partly the mining shafts are associated with Relict Charcoal Hearths (RCH), another small form which is a result of charcoal production. In the Mala Panew River valley, north of Tarnowsky Gory, several tens of thousands of these RCH are found, which could be mapped by LiDAR in recent years. More detailed pedological investigations, which would allow a systematic comparison with other known RCH sites, are missing so far.</p><p>Within the framework of a Polish-German cooperation project, we started in 2021 to investigate the mining shafts and the RCH in Tarnowsky Gory and in the Mala Panew River valley from a pedological-sedimentological point of view. At the RCH sites on the Mala Panew River, we focused on the following questions: How was the soil stratigraphy changed by the RCH construction? What are main processes of soil development before and after RCH construction? What was the role of the pits surrounding the RCH? How do the sites differ from the RCHs at Tarnowsky Gory especially with respect to soil properties and soil genesis? In Tarnowsky Gory, where a RCH was excavated directly next to a mining shaft, the following questions were in focus: How did the mining activity change soil distribution and soil properties? What are main processes of soil development on the different parts? What is the origin of the pit infill? What is the origin of the shaft rim deposits?</p><p>Our work program included the construction of excavator trenches across the mining remains, construction, description and sampling of soil profiles along the trenches, schematic drawing of the soil stratigraphy, and laboratory analyses for the determination of texture, Munsell color, pH (CaCl2, H20), CaCO3 content, Ctotal & Ntotal and total elements by FPXRF. We present the first results of the ongoing investigations.</p>
BoundingBox Localizer Tool (BLT), Geopodoly and Landscape Development department at the Brandenbu... more BoundingBox Localizer Tool (BLT), Geopodoly and Landscape Development department at the Brandenburgische Technische Universität Cottbus-Senftenberg version<br> Developed by W.B. Verschoof-van der Vaart MA & A. Brandsen MSc<br> Faculty of Archaeology / Data Science Research Programme<br> Leiden University, The Netherlands BLT transforms the output of an object detection model (such as Faster R-CNN) into geospatial vectors (polygons) usable in a GIS environment.
Journal of Computer Applications in Archaeology
Generally, the data from remote sensing surveys is screened manually in archaeology. However, con... more Generally, the data from remote sensing surveys is screened manually in archaeology. However, constant monitoring of the earth's surface-by a multitude of airborne and satellite sensors-causes a huge influx of data of high complexity and high quality. To cope with this ever-growing set of largely digital and easily available data, computer-aided methods for the processing of data and the detection of archaeological objects 1 are needed ( Bennett, Cowley & De Laet 2014: 896). Over a decade ago, archaeologists started developing computational methods for the (semi-)automated detection of archaeological objects . Since then multiple case studies have shown these algorithms to be capable of detecting well-defined archaeological traces, such as barrows (see for example ). However, these (often) handcrafted algorithms are highly specialised on specific, single object categories and data sources, which restricts their use in different contexts and limits their usability in general for archaeological prospection. Furthermore, these approaches are predominantly complex algorithms that can require a high level of expertise to operate, and are regularly dependent on expensive software. All this results in an implementation that is limited in its user-friendliness (see also Ball, Anderson & Seng Chan 2017: 3). To overcome the aforementioned limitations, this research project explores the implementation of advanced computational methods to develop a generic, flexible and robust automated detection method for archaeological objects in remotely sensed data. More specifically, this project aims to develop user-friendly workflows for the detection of multiple classes of archaeological objects in LiDAR (Light Detection And Ranging; Wehr & Lohr 1999) data using Deep Learning (Goodfellow, Bengio & Courville 2016). The research project, a four-year PhD, is part of the Data Science Research Programme (DSRP) at the Faculty of Archaeology and the Leiden Centre of Data Science (LCDS) at Leiden University. The DSRP aims to bring together domain knowledge and associated 'big data' problems (for instance in archaeology) with the technical methods and solutions from data science. This paper presents the results of the first year of the PhD project consisting of the first workflow developed, called WODAN (Workflow for Object Detection of Archaeology in the Netherlands). WODAN has successfully been implemented on LiDAR data from the research area in the Netherlands (Figure ). The workflow serves as a proof of concept, to demonstrate that by implementing deep learning techniques it is possible to create a multi-class
Remote Sensing
Although the history of automated archaeological object detection in remotely sensed data is shor... more 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 contributi...
Journal of Computer Applications in Archaeology
Archaeological Prospection, 2021
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Papers by Wouter Verschoof-van der Vaart
For four 32.25 km2 case study areas in different geogenetic regions of the Netherlands, prehistoric land use surface areas are modelled based on conventional methods and the results are compared to the results we obtained by using AI-assisted detection of prehistoric field systems. The nationally available LiDAR data were used for automated detection. Geotiff DTM images were fed into an object detection algorithm (based on the YOLOv4 framework and trained with known Dutch sites), and resultant geospatial vectors were imported into GIS.
Our analysis shows that AI-assisted detection of prehistoric embanked field systems on average leads to a factor 1.84 increase in known surface areas of Celtic fields. Modelling the numbers of occupants from this spatial coverage, yields population sizes of 37–135 persons for the case study regions (i.e. 1.15 to 4.19 p/km2). This range aligns well with previous estimates and offers a more robust and representative proxy for palaeodemographic reconstructions. Variations in land use coverage between the regions could be explained by differences in present-day land use and research intensity. Particularly the regionally different extent of forestlands and heathlands (ideal for the (a) preservation and (b) automated LiDAR detection of embanked field systems) explains minor variations between the four case study regions.
For four 32.25 km2 case study areas in different geogenetic regions of the Netherlands, prehistoric land use surface areas are modelled based on conventional methods and the results are compared to the results we obtained by using AI-assisted detection of prehistoric field systems. The nationally available LiDAR data were used for automated detection. Geotiff DTM images were fed into an object detection algorithm (based on the YOLOv4 framework and trained with known Dutch sites), and resultant geospatial vectors were imported into GIS.
Our analysis shows that AI-assisted detection of prehistoric embanked field systems on average leads to a factor 1.84 increase in known surface areas of Celtic fields. Modelling the numbers of occupants from this spatial coverage, yields population sizes of 37–135 persons for the case study regions (i.e. 1.15 to 4.19 p/km2). This range aligns well with previous estimates and offers a more robust and representative proxy for palaeodemographic reconstructions. Variations in land use coverage between the regions could be explained by differences in present-day land use and research intensity. Particularly the regionally different extent of forestlands and heathlands (ideal for the (a) preservation and (b) automated LiDAR detection of embanked field systems) explains minor variations between the four case study regions.