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

Karsten Lambers,Wouter Verschoof-Van Der Vaart,Quentin Bourgeois

doi:10.3390/rs11070794

Abstract

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.

Highlights

We argue that the combination and integration of all three approaches—machine learning-based object detection, citizen science-based online data interpretation and revision, and a citizen science fieldwork campaign—will provide an integrated approach that will be beneficial to all three elements
Based on the results and challenges presented by automated object detection techniques, the opportunities offered by citizen science, and the current research strategy for validating new potential archaeological objects from remotely sensed data, we propose an innovative integrated workflow to generate datasets for machine learning approaches and to validate new potential archaeological objects
The integrated workflow that we introduce here and are currently employing in our Veluwe case study is a novel combination of methods from remote sensing, machine learning, and citizen science for the purpose of archaeological prospection

Summary

Introduction

The importance of remote sensing as a data source for archaeological prospection has grown exponentially in recent years [1,2]. Sensed data from terrestrial, aerial, and spaceborne sensors are today a key element of local and regional scale archaeological research, as well as heritage management [3]. A recent study from Scotland [4] even advocates the primary reliance on remotely sensed data as part of a national archaeological mapping strategy. This is in line with the growing importance of non-invasive research strategies that enable the preservation of archaeological heritage [5,6,7].

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Discussion

Conclusion