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Article

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

1
Faculty of Archaeology, Leiden University, P.O. Box 9514, 2300 RA Leiden, The Netherlands
2
Data Science Research Programme (Leiden Centre of Data Science), Leiden University, P.O. Box 9505, 2300 RA Leiden, The Netherlands
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(7), 794; https://doi.org/10.3390/rs11070794
Received: 1 March 2019 / Revised: 27 March 2019 / Accepted: 29 March 2019 / Published: 3 April 2019
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. View Full-Text
Keywords: airborne laser scanning; archaeological prospection; deep learning; citizen science; The Netherlands airborne laser scanning; archaeological prospection; deep learning; citizen science; The Netherlands
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MDPI and ACS Style

Lambers, K.; Verschoof-van der Vaart, W.B.; Bourgeois, Q.P.J. Integrating Remote Sensing, Machine Learning, and Citizen Science in Dutch Archaeological Prospection. Remote Sens. 2019, 11, 794. https://doi.org/10.3390/rs11070794

AMA Style

Lambers K, Verschoof-van der Vaart WB, Bourgeois QPJ. Integrating Remote Sensing, Machine Learning, and Citizen Science in Dutch Archaeological Prospection. Remote Sensing. 2019; 11(7):794. https://doi.org/10.3390/rs11070794

Chicago/Turabian Style

Lambers, Karsten, Wouter B. Verschoof-van der Vaart, and Quentin P.J. Bourgeois. 2019. "Integrating Remote Sensing, Machine Learning, and Citizen Science in Dutch Archaeological Prospection" Remote Sensing 11, no. 7: 794. https://doi.org/10.3390/rs11070794

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