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

III：媒介：协作研究：城市和考古恢复的深度生成模型

• 批准号: 2106766
• 负责人: Nathaniel VanValkenburgh
• 金额: $ 7.14万
• 依托单位: Brown University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2106766&HistoricalAwards=false
• 关键词: III; Medium; Collaborative; Research; Deep

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.

对人类历史进程中城市化的演变进行建模和理解，阐明了人类文明的一个关键方面，可以大大帮助当今的利益相关者为未来的城市发展做出更明智的决策。然而，当前和过去城市空间的建模仍然极具挑战性，并且缺乏古代和现代城市形式之间的严格比较。在该项目中，该团队将提供一个基于人工智能的框架，用于从稀疏的遥感和实地观察中发现相对复杂的城市模型（墙壁、角落、房间、方向和建筑区域集群）。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。该研究小组将提供一个初步的网络基础设施，追求历史遗址的三维重建，创建一个功能和基于时间的古代遗址的城市分类，从晚期前西班牙和殖民时期安第斯山脉和青铜/铁器时代南高加索时期，同时利用NEH和美国理事会资助的GeoPACHA网络平台的结果传播。此外，该项目跨越美国三所主要大学和五个系，由五名经验丰富的高级研究员和至少六名多学科研究生组成的团队领导，以及额外的本科生，他们将在顶级场所发表论文，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过程中的城市化演变，该项目寻求一种计算方法，用于从稀疏数量的观测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，但对历史城市站点进行建模从根本上限于稀疏（很少）的数据观测，因为大多数结构都已被侵蚀或破坏。推理方法显示出显著的前景，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程序，该小组将协助计算考古学家有相对稀疏的数据，但一个基本的结构化网站。首先，他们将解决一个集合覆盖问题，以确定一组离散的原子元素和规则，这些元素和规则是最小的，但足以覆盖稀疏数据。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，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来模拟考古遗址，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调整，以协助其他领域也限于稀疏观测的基础结构region.This奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的智力价值和更广泛的影响审查标准的支持。

项目成果

Semi-supervised contrastive learning for remote sensing: identifying ancient urbanization in the south-central Andes

• DOI: 10.1080/01431161.2023.2192879
• 发表时间: 2021-12
• 期刊: International Journal of Remote Sensing
• 影响因子: 3.4
• 作者: Jiachen Xu;James Zimmer-Dauphinee;Quan Liu;Yuxuan Shi;Steven A. Wernke;Yuankai Huo