Quentin Bourgeois
Biographic Data
| ID | 1344717 |
|---|---|
| NAME | Quentin Bourgeois |
| GIVEN NAMES | Quentin |
| FAMILY NAME | Bourgeois |
| SIGNATURE | BOURGEOIS Q |
| AFFILIATIONS | Leiden University |
| ORCID | 0000-0001-7518-2142 |
| VERIFIED | Yes |
| TOTAL WORKS | 6 |
| TOTAL CITATIONS | 6 |
| WORKS AS AUTHOR | 6 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2019 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 2 |
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…
The Indo-European Puzzle Revisited
This book examines the impact of ancient DNA research and scientific evidence on our understanding of the emergence of Indo-European languages in prehistory. Offering cutting-edge contributions from an international team of scholars, it considers the driving forces behind the Indo-European migrations during the 3rd and 2nd millenia BC. The volume explores the rise of the world's first pastoral nomads the Yamnaya Culture in the Russian Pontic step…
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…
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…
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…
The introduction of Corded Ware Culture at a local level
The introduction of the Corded Ware Culture (3000–2500 BCE) is considered a formative event in Europe's past. Ancient DNA analyses demonstrate that migrations played a crucial role in this event. However, these analyses approach the issue at a supra-regional scale, leaving questions about the regional and local impact of this event unresolved. This study pilots an approach to ceramics that brings this small-scale impact into focus by using the tr…
The introduction of Corded Ware Culture at a local level
The introduction of the Corded Ware Culture (3000–2500 BCE) is considered a formative event in Europe's past. Ancient DNA analyses demonstrate that migrations played a crucial role in this event. However, these analyses approach the issue at a supra-regional scale, leaving questions about the regional and local impact of this event unresolved. This study pilots an approach to ceramics that brings this small-scale impact into focus by using the tr…
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…
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…
The introduction of Corded Ware Culture at a local level
The introduction of the Corded Ware Culture (3000–2500 BCE) is considered a formative event in Europe's past. Ancient DNA analyses demonstrate that migrations played a crucial role in this event. However, these analyses approach the issue at a supra-regional scale, leaving questions about the regional and local impact of this event unresolved. This study pilots an approach to ceramics that brings this small-scale impact into focus by using the tr…
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…
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…
The Indo-European Puzzle Revisited
This book examines the impact of ancient DNA research and scientific evidence on our understanding of the emergence of Indo-European languages in prehistory. Offering cutting-edge contributions from an international team of scholars, it considers the driving forces behind the Indo-European migrations during the 3rd and 2nd millenia BC. The volume explores the rise of the world's first pastoral nomads the Yamnaya Culture in the Russian Pontic step…
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…
Archaeology (5 works) · Geography (5 works) · Archaeological Research and Protection (4 works) · Remote sensing (4 works) · 3D Surveying and Cultural Heritage (3 works) · History (3 works) · Archaeology and ancient environmental studies (2 works) · Cartography (2 works) · Citizen science (2 works) · Computer Science (2 works)