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.

中文翻译：

---

基于深度学习的考古地球物理图像自动分析

得益于深度学习（DL）的最新进展以及大型的带有标签/注释的数据集和经过训练的网络模型的日益普及，在自动分析来自不同科学领域（例如医学，微生物学，天文学和遥感）的图像方面取得了令人瞩目的进展。考古地球物理数据的自动分析也被认为很重要，这是因为景观调查所覆盖区域的空间范围很大，使用机动小车驱动的多传感器阵列进行了景观调查，并随后收集了大量数据。在这项工作中，通过Python 3.6编程语言，使用带有Tensorflow后端的Keras深度学习库，通过卷积神经网络（CNN）构建了卷积神经网络（CNN）。