Physically Based Predictive Modelling of Archaeological Proxies Using Cropmarks

Datos Bibliográficos

ID12430010
AutoresElias Gravanis (0000-0002-5331-6661, Department of Civil Engineering and Geomatics Cyprus University of Technology Limassol Cyprus, autor de correspondencia), Athos Agapiou (0000-0001-9106-6766, Department of Civil Engineering and Geomatics Cyprus University of Technology Limassol Cyprus)
Año2025
Volumen33
Número1
Páginas177-187
Fecha de publicación2025-11-10
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaArchaeological Prospection (JOURNAL)
Identificadores de la revistaISSN: 1075-2196 • E-ISSN: 1099-0763
EditorialWiley (PUBLISHER • GB)
DOI10.1002/arp.70015
OpenAlexW4416136029
IdiomaEN
Referencias citadas35

Cropmarks, as archaeological proxies, offer a valuable means of detecting buried sites through remote sensing. Yet, the scalability of such methods across varied archaeological contexts remains underexplored, and AI‐based modelling approaches are still in early stages. This gap stems from environmental variability, limited ground‐truth data due to intrusive validation and insufficient collaboration between archaeology and computer science. In this study, we assess the predictive performance of machine learning models trained on synthetic spectral signatures—representing cropmarks and healthy crops—generated via PROSAIL‐based radiative transfer inversion. These synthetic data serve as a necessary augmentation of the original observations for model training and statistical analysis. These models are tested on new observations from the same test field, collected in a different year. Our ensemble approach uses multiple classifiers trained on batches of synthetic samples with majority voting and explicitly examines the impact of input noise. Results show detection rates of up to 92% in specific cases, with peak accuracy achieved when the synthetic dataset is twice the size of the original observations. These findings underscore the potential of radiative transfer–based synthetic data to support scalable cropmark detection in both new and archival hyperspectral datasets

Hyperspectral imaging · Predictive modelling · Radiative transfer · Scalability · Statistical model · Synthetic data · Transfer of learning · Archaeological Research and Protection · Archaeology and ancient environmental studies · Cultural Heritage Materials Analysis