3D Scotland in miniature: digital topography, deep neural networks & approaches to national mapping
Channel: Recording Archaeology
Date: 2018-12-17

The increasing availability of extensive complex datasets, including Airborne Laser Scanning (ALS) data, is a world of opportunity. It is also a challenge to develop work practices that can explore the potential archaeological knowledge dividend effectively, especially for the creation of systematic national-scale heritage inventories which are a foundation for heritage management and research. This paper presents work on the Island of Arran, often referred to as ‘Scotland in miniature’ because it encompasses many of the landscape types of the country as a whole. Historic Environment Scotland is using this outdoor laboratory to develop approaches to extensive and rapid archaeological landscape mapping and the exploration of computational approaches to object detection using deep neural networks.

One strand of this work concerns manual mapping of the archaeological landscape. This foregrounds remotely sensed data (ALS) as the primary source of information, supplemented by orthophotographs and field observation, requiring multi-scaled desk-based interpretation of ALS derivatives, the documentation of certainty of interpretation, and an iterative, selective approach to field observation. The latter is used primarily to improve the interpretational certainty of those desk-based identifications with low confidence levels. This approach has achieved considerable increases in rates of area coverage over methods of field survey that largely rely on field walking to cover the ground. For Arran, the project has also generated a three-fold increase in the number of known monuments.
The second strand of the work, undertaken with the Norwegian Computing Center, is exploring the potential of deep learning for national mapping of cultural heritage. This approach is based on the height data and learning sets comprising the centre positions of known archaeological remains. The deep neural network used in this work had already been pre-trained on one million images of natural scenes, and outputs a vector of presence/absence of objects of potential interest. Cross-validation of outputs with known cultural heritage remains, supplemented by field checking, indicates a success rate, so far, of about 90% positive identifications.

This paper concludes with thoughts on how the combination of approaches tested on Arran can be extended to support the cost-effective and rapid national mapping of Scotland.

Dave Cowley, Øivind Due Trier, Łukasz Banaszek, Anders Ueland Waldeland