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.

在人类历史过程中建模和理解城市化的演变阐明了人类文明的关键方面，并且可以大大帮助利益相关者为未来的城市发展做出更好的知情决定。但是，当前和过去的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。在这个项目中，团队将提供一个基于人工智能的框架，以发现相对复杂的城市模型（墙壁，角落，房间，方向和建筑面积簇），从稀疏数量的遥感和现场观测中发现。与今天在场的城市相比，建模历史城市地点从根本上仅限于稀疏（且很少）的数据观察结果，因为大多数结构已被侵蚀或破坏。研究团队将提供初步的网络基础设施，追求历史遗迹的3D重新创建，创建一个基于特征和时间的城市分类，对较晚的前卫生和殖民时期安第斯山脉和青铜/铁器时代南方高加索期的古代遗址，同时利用了Soceries conferting socecaties fultecation confloce confertical fultecation fultecons fultecation fultecties fultection futheries fultection。此外，该项目跨越了美国三所主要大学和五个部门，由五名经验丰富的高级研究人员和一支由六个由六个多学科研究生组成来自稀疏观测的城市模型。尽管对当前城市进行密集的收购意味着关注传感器部署和大数据问题，但建模历史城市地点从根本上限于稀疏（少）数据观察，因为大多数结构已被侵蚀或破坏。推论方法表现出巨大的希望，但它们在相对稀疏的数据和结构晦涩的情况下挣扎。作为第一个域应用，团队将协助具有相对稀疏数据但基础结构化站点的计算考古学家。首先，他们将解决一个设定的覆盖问题，以确定一组离散的原子元素和规则，这些元素和规则最少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以自我监管的方式生成足够的数据样本，以培训深层网络，以学习如何执行细分，分类和完成。最后，他们将使用学识渊博的表示形式对考古遗址进行建模，从而导致重建，语义理解和现场分类学。此外，该团队预计可以重新进行开发的模型以协助其他领域，还仅限于对基本结构化区域的稀疏观察。该奖项反映了NSF的法定任务，并且认为值得通过基金会的知识分子优点评估来支持，并具有更广泛的影响标准。

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