Deep learning based automated analysis of archaeo-geophysical images

Abstract

Thanks to recent advances in deep learning (DL) and the increasing availability of large labeled/annotated datasets and trained network models, there has been impressive progress in the automated analysis of images from different scientific domains such as medicine, microbiology, astronomy and remote sensing. The automated analysis of archaeo-geophysical data is also considered important due to the large spatial extent of areas covered by landscape surveys using multi-sensor arrays driven by motorized carts and subsequently the large volume of collected data. In this work, a convolutional neural network (CNN) is built by Python 3.6 programming language using the Deep Learning Library of Keras with Tensorflow backends, a library that implements the building blocks for CNN. The network is trained from scratch adopting U-Net architecture to accomplish an automatic analysis of the archaeo-geophysical features with emphasis on ground-penetrating radar (GPR) anomalies.