III: Medium: Collaborative Research: Deep Generative Modeling for Urban and Archaeological Recovery

对人类历史进程中城市化演变的建模和理解阐明了人类文明的一个关键方面，可以极大地帮助今天的利益相关者为未来的城市发展做出更明智的决策。然而，现在和过去的城市空间建模仍然极具挑战性，古代和现代城市形态之间缺乏严格的比较。在这个项目中，团队将提供一个基于人工智能的框架，用于从少量的遥感和实地观测中发现相对复杂的城市模型（墙壁、角落、房间、方向和建成区域集群）。与今天的城市相反，对历史城市遗址进行建模基本上仅限于稀疏（而且很少）的数据观测，因为大多数结构都已被侵蚀或破坏。研究小组将提供一个初步的网络基础设施，对历史遗址进行3D重建，创建一个基于特征和时间的古代遗址城市分类，涵盖前西班牙晚期和殖民时期的安第斯山脉和青铜/铁器时代的南高加索时期，同时利用NEH和美国学术学会理事会资助的GeoPACHA网络平台进行结果传播。此外，该项目横跨美国三所主要大学和五个系，由五名经验丰富的高级研究人员和至少六名多学科研究生以及额外的本科生组成的团队领导，他们将在顶级场所发表出版物，会议研讨会，以及论文和博士论文。为了帮助建模和理解人类历史进程中城市化的演变，该项目寻求一种计算方法，用于从稀疏的观测数据中发现相对复杂的城市模型。虽然对当前城市进行密集采集意味着关注传感器部署和大数据问题，但对历史城市遗址进行建模基本上仅限于稀疏（很少）的数据观测，因为大多数结构都已被侵蚀或破坏。推理方法显示出重大的前景，但它们在相对稀疏的数据和模糊的结构的情况下挣扎。作为第一个领域的应用，该团队将帮助计算考古学家拥有相对稀疏的数据，但有一个潜在的结构化遗址。首先，他们将解决一个集合覆盖问题，以确定最小但足以跨越稀疏数据的原子元素和规则的离散集。其次，他们将使用这些原子元素和规则来产生足够的数据样本，以自我监督的方式训练深度网络，以学习如何执行分割、分类和补全。最后，他们将使用学习到的表征对考古遗址进行建模，例如重建、语义理解和遗址分类。此外，该团队预计，开发的模型可以重新工具，以协助其他领域，也仅限于对底层结构区域的稀疏观察。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Modeling and understanding the evolution of urbanization over the course of human history elucidates a key aspect of human civilization, and can significantly help stakeholders today make better informed decisions for future urban development. However, the modeling of current and past urban spaces remains extremely challenging and a rigorous comparison between ancient and modern urban form is lacking. In this project, the team will provide an artificial intelligence based framework for discovering a relatively complex urban model (walls, corners, rooms, orientation, and built area clusters) from a sparse number of remote sensing and field observations. As opposed to cities present today, modeling a historical urban site is fundamentally limited to sparse (and few) data observations because most of the structures have been eroded or destroyed. The research team will provide a preliminary cyberinfrastructure, pursue 3D re-creations of historical sites, create a feature- and time-based urban taxonomy of ancient sites from the late Prehispanic and Colonial period Andes and the Bronze/Iron Age South Caucasus periods, while leveraging the NEH and American Council of Learned Societies funded GeoPACHA web platform for result dissemination. Moreover, the project spans three major US universities and five departments, led by five experienced senior researchers and a team of at least six multidisciplinary graduate students, as well as additional undergraduates, who will produce publications in top tier venues, conference workshops, as well as theses and PhD dissertations.To assist with modeling and understanding the evolution of urbanization over the course of human history, this project seeks a computational methodology for discovering a relatively complex urban model from a sparse number of observations. While performing a dense acquisition of a current city implies focusing on sensor deployment and on big data issues, modeling a historical urban site is fundamentally limited to sparse (and few) data observations because most of the structures have been eroded or destroyed. Inferencing approaches show significant promise, but they struggle in a situation of relatively sparse data and obscured structure. As a first domain application, the team will assist computational archaeologists having relatively sparse data but of an underlying structured site. First, they will solve a set cover problem to determine a discrete set of atomic elements and rules that are minimal yet sufficient to span the sparse data. Second, they will use these atomic elements and rules to produce sufficient data samples for training deep networks in a self-supervised manner in order to learn how to perform segmentation, classification, and completion. Finally, they will use the learned representations to model archaeological sites resulting in reconstructions, semantic understandings, and site taxonomies, for instance. Further, the team anticipates that the developed models can be re-tooled to assist with other domains also limited to sparse observations of an underlying structured region.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.