Field systems and later prehistoric land use: New insights into land use detectability and palaeodemography in the Netherlands through LiDAR, automatic detection and traditional field data

Abstract

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 barrows. We argue that prehistoric field systems of field plots beset on all sides by earthen banks—known as Celtic fields—are a more suitable (i.e. less nodal) proxy for reconstructing later prehistoric land use.

For four 32.25 km² case study areas in different geogenetic regions of the Netherlands, prehistoric land use surface areas are modelled based on conventional methods and the results are compared to the results we obtained by using AI-assisted detection of prehistoric field systems. The nationally available LiDAR data were used for automated detection. Geotiff DTM images were fed into an object detection algorithm (based on the YOLOv4 framework and trained with known Dutch sites), and resultant geospatial vectors were imported into GIS.

Our analysis shows that AI-assisted detection of prehistoric embanked field systems on average leads to a factor 1.84 increase in known surface areas of Celtic fields. Modelling the numbers of occupants from this spatial coverage, yields population sizes of 37–135 persons for the case study regions (i.e. 1.15 to 4.19 p/km²). This range aligns well with previous estimates and offers a more robust and representative proxy for palaeodemographic reconstructions. Variations in land use coverage between the regions could be explained by differences in present-day land use and research intensity. Particularly the regionally different extent of forestlands and heathlands (ideal for the (a) preservation and (b) automated LiDAR detection of embanked field systems) explains minor variations between the four case study regions.