Imaging the past using methods of the future: computer-aided interpretation of ground-penetrating radar data collected at Roman towns in Italy

由于其广泛性，地球物理勘探非常适合研究罗马城市的整体特征和发展。虽然目前我们的理解依赖于一些大部分挖掘的遗址（例如庞贝，奥斯蒂亚），但勘探可以帮助回答有关城市规划，街道网络，居住区规模，人口估计以及城市边界和城市发展之间的相互作用的问题。然而，一个关键的瓶颈是地球物理数据的传统解释（人工描绘异常）的耗时特性。最能说明这一点的是探地雷达（GPR）技术，该技术揭示了罗马城镇的详细规划，但由于其高分辨率和三维性质，产生了大量数据。如今，强大的机器学习技术（如深度卷积神经网络（DCNN））用于探矿数据的计算机辅助解释（CAI）的潜力越来越多地得到证明。然而，DCNN需要大量的手动划定的训练集。建立这些是一种努力，部分抵消了CAI的好处。在这个项目中，我将研究新的方法，从少量的手动划定训练DCNN（同时提供有竞争力的结果），并将其应用于来自意大利罗马城镇的GPR数据。此外，我将结合联合收割机这些和其他CAI算法在一个用户友好的工具箱，可以在考古界传播。最后，CAI结果的基础上，我将提供一个考古解释的GPR数据集从Falerii诺维（意大利中部）使用GIS，数据融合和空间语法。我将接受罗马城市规划和机器学习方面的培训，并在一家商业软件开发商的借调期间接受计算技能方面的培训。通过非侵入性的调查，这个跨学科的项目将提高对罗马城市化的理解，并促进这些重要文化遗产的保护。

Because of its extensive character, geophysical prospection is well-suited to investigate the overall character and development of Roman cities. Whereas currently our understanding relies on a few largely excavated sites (e.g. Pompeii, Ostia), prospection can help answering questions on city planning, street network, the size of inhabited areas, population estimations, and the interaction between city boundaries and urban development. However, a key bottleneck is the time consuming character of the traditional interpretation of geophysical data (manual delineation of anomalies). This is best illustrated by the ground-penetrating radar (GPR) technique, which reveals detailed plans of Roman towns, but produces enormous amounts of data because of its high-resolution and 3-D nature. Today, the potential of powerful machine learning techniques such as deep convolutional neural networks (DCNNs) for the computer-aided interpretation (CAI) of prospection data is increasingly demonstrated. However, DCNNs need large, manually delineated training sets. Building these is an effort that partially undoes the benefit of CAI. In this project, I will investigate novel approaches that train DCNNs from a small amount of manual delineations (while delivering competitive results), and apply them to GPR data from Roman towns in Italy. Furthermore, I will combine these and other CAI algorithms in a user-friendly toolbox that can be disseminated among the archaeological community. Finally, on the basis of the CAI results, I will provide an archaeological interpretation of the GPR dataset from Falerii Novi (Central-Italy) using GIS, data fusion and space syntax. I will be trained in Roman urbanism and machine learning, and in computing skills during a secondment at a commercial software developer. By stimulating non-invasive investigation, this interdisciplinary project will enhance the understanding of Roman urbanism, and promote the preservation of these important cultural heritage sites.