Convolutional neural networks for accurate identification of mining remains from UAV-derived images

Abstract

A new deep learning system is proposed for the rapid and accurate identification of anthropogenic elements of the Roman mining infrastructure in NW Iberia, providing a new approach for automatic recognition of different mining elements without the need for human intervention or implicit subjectivity. The recognition of archaeological and other abandoned mining elements provides an optimal test case for decision-making and management in a broad variety of research fields. A new image dataset was created by obtaining UAV images from different anthropic features. A convolutional neural network architecture was implemented, achieving recognition results of close to 95% accuracy. This methodological approach is suitable for the identification and accurate location of ancient mines and hydrologic infrastructure, providing new tools for accurate mapping of mining landforms. Additionally, this novel application of deep learning can be implemented to reduce potential risks caused by abandoned mines, which can cause significant annual human and economic losses worldwide.