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

模拟和理解城市化在人类历史进程中的演变阐明了人类文明的一个关键方面，并可以显著帮助今天的利益攸关方为未来的城市发展做出更明智的决策。然而，对现在和过去的城市空间的建模仍然具有极大的挑战性，缺乏对古代和现代城市形态的严格比较。在这个项目中，该团队将提供一个基于人工智能的框架，用于从稀少的遥感和现场观察中发现相对复杂的城市模型(墙、角落、房间、方向和建成的区域群)。与现在的城市不同，对历史城市遗址的建模基本上局限于稀少的(和很少的)数据观测，因为大多数结构已经被侵蚀或摧毁。研究小组将提供初步的网络基础设施，进行历史遗址的3D重建，创建基于特征和时间的安第斯前西班牙晚期和殖民地时期以及青铜/铁器时代南高加索时期的古迹城市分类法，同时利用NEH和美国学会理事会资助的GeoPACHA网络平台传播结果。此外，该项目横跨美国三所主要大学和五个系，由五名经验丰富的资深研究人员和至少六名多学科研究生组成的团队领导，以及其他本科生，他们将在顶级会场、会议研讨会以及论文和博士学位论文中发表出版物。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，从稀少的观察中发现相对复杂的城市模型。虽然对当前城市进行密集收购意味着要专注于传感器部署和大数据问题，但对历史城市遗址进行建模从根本上仅限于稀少(且很少)的数据观测，因为大多数结构已被侵蚀或摧毁。推理方法显示出巨大的前景，但它们在数据相对稀少、结构模糊的情况下举步维艰。作为第一个领域应用，该团队将协助具有相对稀疏数据但底层结构化遗址的计算考古学家。首先，他们将解决集合覆盖问题，以确定原子元素和规则的离散集合，这些元素和规则最小，但足以跨越稀疏数据。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，用于以自我监督的方式训练深度网络，以便学习如何执行分割、分类和完成。最后，他们将使用学习到的表示法来模拟考古遗址，例如，导致重建、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装备，以帮助其他领域也限于对基础结构区域的稀疏观察。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.