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

使用未来的方法想象过去：对在意大利罗马城镇收集的探地雷达数据进行计算机辅助解释

• 批准号: EP/X024474/1
• 负责人: Alessandro Launaro
• 金额: $ 26万
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FX024474%2F1
• 关键词: Imaging; past; using; methods; future

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.

由于其广泛性，地球物理勘探非常适合于研究罗马城市的总体特征和发展。虽然目前我们的理解依赖于几个大量挖掘的遗址(例如庞贝、奥斯提亚)，但展望有助于回答城市规划、街道网络、居民区大小、人口估计以及城市边界和城市发展之间的相互作用等问题。然而，一个关键的瓶颈是传统的地球物理数据解释(人工圈定异常)耗时的特点。探地雷达(GPR)技术最好地说明了这一点，该技术揭示了罗马城镇的详细规划，但由于其高分辨率和3-D性质，产生了大量数据。如今，深度卷积神经网络(DCNN)等强大的机器学习技术在勘探数据计算机辅助解释(CAI)中的潜力日益显现。然而，DCNN需要大量的手动描述的训练集。构建这些是一项部分抵消了计算机辅助教学的好处的努力。在这个项目中，我将研究一些新的方法，这些方法从少量的手工描述中训练DCNN(同时提供有竞争力的结果)，并将它们应用于来自意大利罗马城镇的GPR数据。此外，我将把这些算法和其他计算机辅助教学算法结合在一个用户友好的工具箱中，可以在考古界传播。最后，在计算机辅助教学结果的基础上，我将利用地理信息系统、数据融合和空间句法对意大利中部法勒里奥诺维的探地雷达数据集进行考古解释。我将接受罗马都市主义和机器学习方面的培训，并在借调到一家商业软件开发公司期间接受计算技能方面的培训。通过刺激非侵入性调查，这个跨学科的项目将加强对罗马城市主义的理解，并促进这些重要文化遗产的保护。