Wouter B Verschoof-Van Der Vaart
Biographic Data
| ID | 914432 |
|---|---|
| NAME | Wouter B Verschoof-Van Der Vaart |
| GIVEN NAMES | Wouter B Verschoof |
| FAMILY NAME | Van Der Vaart |
| SIGNATURE | VAN DER VAART W B V |
| AFFILIATIONS | Leiden University |
| ORCID | 0000-0002-1053-3009 |
| VERIFIED | Yes |
| TOTAL WORKS | 12 |
| TOTAL CITATIONS | 20 |
| WORKS AS AUTHOR | 12 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2019 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 2 |
ARCfieldLAB
Climate change, urban expansion, and agricultural intensification are increasingly threatening the Netherlands’ in situ archaeological heritage, necessitating the use of advanced methodologies for effective detection, mapping, characterizing, and monitoring of archaeological sites. Over the past decade, significant advancements in sensor technologies for remote sensing and geophysics have emerged that offer more effective, noninvasive solutions i…
Land Use Legacy Landforms at the Unesco Heritage Site Tarnowskie Góry, Upper Silesia, Poland—Stratigraphy, Soils and Age
Metallurgy in Upper Silesia (Poland) has a long tradition of international significance, which was emphasized in 2017 when the historic silver mine in Tarnowskie Góry was inscribed as a UNESCO World Heritage site. The area consists of various anthropogenic landforms, the stratigraphy of which has hardly been studied so far. In this study, we describe five main morphological units: Unit I (RCH) and Unit II (RCH pit), resulting from charcoal produc…
Assessing the quality of citizen science in archaeological remote sensing
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 agree…
Multi-Period Ore Exploitation in Upper Silesia, Central Europe
We studied the abandoned mining field in southern Poland using high-resolution lidar images to distinguish the number of mining shaft remains and their morphological diversity. We identified 13,864 remains of mining shafts of various sizes (2–30 m in diameter) and diverse levels of denudation. This allowed us to select 13 mining shafts for detailed study. The radiocarbon dating of charcoal and peat from the shafts indicate three phases of exploit…
Field systems and later prehistoric land use
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 b…
Automated large‐scale mapping and analysis of relict charcoal hearths in Connecticut (USA) using a Deep Learning Yolov4 framework
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 north‐eastern 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.…
Implementing State-of-the-Art Deep Learning Approaches for Archaeological Object Detection in Remotely-Sensed Data
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 resea…
Applying automated object detection in archaeological practice
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 …
Combining Deep Learning and Location-Based Ranking for Large-Scale Archaeological Prospection of LiDAR Data from The Netherlands
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 archaeolo…
Using CarcassonNet to automatically detect and trace hollow roads in LiDAR data from the Netherlands
The systematic mapping of hollow roads, traces of (post)medieval sunken cart tracks ways, can provide information on past human movement and historical route networks. However, the sheer amount of traces and of available high-quality data necessitates the use of computational methods for the automatic detection of these archaeological objects. Therefore, a novel approach, named CarcassonNet, has been developed that uses a combination of a Deep Le…
Integrating Remote Sensing, Machine Learning, and Citizen Science in Dutch Archaeological Prospection
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 c…
Learning to Look at LiDAR
Computer-aided methods for the automatic detection of archaeological objects are needed to cope with the ever-growing set of largely digital and easily available remotely sensed data. In this paper, a promising new technique for the automated detection of multiple classes of archaeological objects in LiDAR data is presented. This technique is based on R-CNNs (Regions-based Convolutional Neural Networks). Unlike normal CNNs, which classify the ent…
Applying automated object detection in archaeological practice
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 …
Using CarcassonNet to automatically detect and trace hollow roads in LiDAR data from the Netherlands
The systematic mapping of hollow roads, traces of (post)medieval sunken cart tracks ways, can provide information on past human movement and historical route networks. However, the sheer amount of traces and of available high-quality data necessitates the use of computational methods for the automatic detection of these archaeological objects. Therefore, a novel approach, named CarcassonNet, has been developed that uses a combination of a Deep Le…
Multi-Period Ore Exploitation in Upper Silesia, Central Europe
We studied the abandoned mining field in southern Poland using high-resolution lidar images to distinguish the number of mining shaft remains and their morphological diversity. We identified 13,864 remains of mining shafts of various sizes (2–30 m in diameter) and diverse levels of denudation. This allowed us to select 13 mining shafts for detailed study. The radiocarbon dating of charcoal and peat from the shafts indicate three phases of exploit…
Field systems and later prehistoric land use
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 b…
Automated large‐scale mapping and analysis of relict charcoal hearths in Connecticut (USA) using a Deep Learning Yolov4 framework
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 north‐eastern 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.…
Land Use Legacy Landforms at the Unesco Heritage Site Tarnowskie Góry, Upper Silesia, Poland—Stratigraphy, Soils and Age
Metallurgy in Upper Silesia (Poland) has a long tradition of international significance, which was emphasized in 2017 when the historic silver mine in Tarnowskie Góry was inscribed as a UNESCO World Heritage site. The area consists of various anthropogenic landforms, the stratigraphy of which has hardly been studied so far. In this study, we describe five main morphological units: Unit I (RCH) and Unit II (RCH pit), resulting from charcoal produc…
Integrating Remote Sensing, Machine Learning, and Citizen Science in Dutch Archaeological Prospection
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 c…
Learning to Look at LiDAR
Computer-aided methods for the automatic detection of archaeological objects are needed to cope with the ever-growing set of largely digital and easily available remotely sensed data. In this paper, a promising new technique for the automated detection of multiple classes of archaeological objects in LiDAR data is presented. This technique is based on R-CNNs (Regions-based Convolutional Neural Networks). Unlike normal CNNs, which classify the ent…
Combining Deep Learning and Location-Based Ranking for Large-Scale Archaeological Prospection of LiDAR Data from The Netherlands
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 archaeolo…
Using CarcassonNet to automatically detect and trace hollow roads in LiDAR data from the Netherlands
The systematic mapping of hollow roads, traces of (post)medieval sunken cart tracks ways, can provide information on past human movement and historical route networks. However, the sheer amount of traces and of available high-quality data necessitates the use of computational methods for the automatic detection of these archaeological objects. Therefore, a novel approach, named CarcassonNet, has been developed that uses a combination of a Deep Le…
Implementing State-of-the-Art Deep Learning Approaches for Archaeological Object Detection in Remotely-Sensed Data
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 resea…
Applying automated object detection in archaeological practice
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 …
Field systems and later prehistoric land use
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 b…
Automated large‐scale mapping and analysis of relict charcoal hearths in Connecticut (USA) using a Deep Learning Yolov4 framework
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 north‐eastern 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.…
Multi-Period Ore Exploitation in Upper Silesia, Central Europe
We studied the abandoned mining field in southern Poland using high-resolution lidar images to distinguish the number of mining shaft remains and their morphological diversity. We identified 13,864 remains of mining shafts of various sizes (2–30 m in diameter) and diverse levels of denudation. This allowed us to select 13 mining shafts for detailed study. The radiocarbon dating of charcoal and peat from the shafts indicate three phases of exploit…
Land Use Legacy Landforms at the Unesco Heritage Site Tarnowskie Góry, Upper Silesia, Poland—Stratigraphy, Soils and Age
Metallurgy in Upper Silesia (Poland) has a long tradition of international significance, which was emphasized in 2017 when the historic silver mine in Tarnowskie Góry was inscribed as a UNESCO World Heritage site. The area consists of various anthropogenic landforms, the stratigraphy of which has hardly been studied so far. In this study, we describe five main morphological units: Unit I (RCH) and Unit II (RCH pit), resulting from charcoal produc…
Assessing the quality of citizen science in archaeological remote sensing
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 agree…
ARCfieldLAB
Climate change, urban expansion, and agricultural intensification are increasingly threatening the Netherlands’ in situ archaeological heritage, necessitating the use of advanced methodologies for effective detection, mapping, characterizing, and monitoring of archaeological sites. Over the past decade, significant advancements in sensor technologies for remote sensing and geophysics have emerged that offer more effective, noninvasive solutions i…
Archaeological Research and Protection (11 works) · Geography (11 works) · Archaeology (10 works) · Remote sensing (9 works) · 3D Surveying and Cultural Heritage (7 works) · Computer Science (6 works) · Artificial Intelligence (5 works) · Conservation Techniques and Studies (5 works) · Geology (5 works) · Lidar (5 works)