Machine learning for the detection of archaeological sites from remote sensor data

Abstract

Deep learning for automated detection of archaeological sites (objects) on remote sensing data is a highly novel field. The key challenge of this field is in the inherent nature of the objects; they occur in small numbers, are sparsely located and feature a unique pattern on the different remote sensing data modalities. To this extent we identify three main contributions, (1) to include multi-sensor data, (2) to optimise Convolutional Neural Networks (CNNs) for small datasets and, (3) to optimise detection of the sparsely located objects. Our results demonstrate that deep learning can be successfully applied to detect archaeological sites on each of the individual remote sensing images, that our efforts to optimise CNNs for small datasets are successful, and that we have discovered new sites that were missed in a manual data analysis and field survey. We have optimised a workflow for the detection of new archaeological sites. We also share the first large-scale publicly available dataset archaeological image classification and object detection along with benchmarks of the most promising models that we applied in this thesis.

More information

Identifiers

ORCID for Jonathon Hare: orcid.org/0000-0003-2921-4283

Catalogue record

Export record