Leiden University Scholarly Publications

Lambers, K.; Verschoof-Van der Vaart, W.B.; Bourgeois, Q.P.J. (2019)

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

Article / Letter to editor

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...Show moreAlthough 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.Show less

Airborne laser scanning

archaeological prospection

deep learning

citizen science

The Netherlands

All authors

Lambers, K.; Verschoof-Van der Vaart, W.B.; Bourgeois, Q.P.J.

Date

2019-04-03

Journal

Remote Sensing

Volume

11

Issue

7

Pages

794

Advanced Publication

Yes

DOI

doi:10.3390/rs11070794

Link

https://doi.org/10.3390/rs11070794

Funding

Sponsorship

NWO

Grant number

275-60-008