Application of Convolutional Neural Networks on Digital Terrain Models for Analyzing Spatial Relations in Archaeology

Archaeological research is increasingly embedding individual sites in archaeological contexts and aims at reconstructing entire historical landscapes. In doing so, it benefits from technological developments in the field of archaeological prospection over the last 20 years, including LiDAR-based Dig...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2022-06, Vol.14 (11), p.2535
Main Authors: Meyer-Heß, M. Fabian, Pfeffer, Ingo, Juergens, Carsten
Format: Article
Language:English
Subjects:
Agriculture
Archaeology
Artificial neural networks
automated detection
Automation
burial mound
CNN
Cost analysis
Datasets
Deep learning
Embedding
Historic sites
hollow way
Infrastructure
landscape archaeology
LiDAR
Memorials & monuments
Mounds
Neural networks
Remote sensing
Terrain models
Visibility
Workflow
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Summary:Archaeological research is increasingly embedding individual sites in archaeological contexts and aims at reconstructing entire historical landscapes. In doing so, it benefits from technological developments in the field of archaeological prospection over the last 20 years, including LiDAR-based Digital Terrain Models, special visualizations, and automated site detection. The latter can generate comprehensive datasets with manageable effort that are useful for answering large-scale archaeological research questions. This article presents a highly automated workflow, in which a Convolutional Neural Network is used to detect burial mounds in the proximity of remotely located hollow ways. Detected mounds are then analyzed with respect to their distribution and a possible spatial relation to hollow ways. The detection works well, produces a reasonable number of results, and achieved a precision of at least 77%. The distribution of mounds shows a clear maximum in the radius of 2000–2500 m. This supports future research such as visibility or cost path analysis.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs14112535