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: 2107096
• 负责人: Daniel Aliaga
• 金额: $ 83.01万
• 依托单位: Purdue 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=2107096&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可以阐明人类文明的一个关键方面，并可以极大地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态之间的严格比较。在这个项目中，该团队将提供一个基于人工智能的框架，用于从少量的遥感和现场观测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）。与当今的城市相反，对历史城市遗址进行建模从根本上仅限于稀疏（且很少）的数据观察，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，对历史遗址进行 3D 重建，对前西班牙晚期和殖民时期安第斯山脉以及青铜/铁器时代南高加索时期的古代遗址创建基于特征和时间的城市分类法，同时利用 NEH 和美国学术团体理事会资助的 GeoPACHA 网络平台来传播结果。此外，该项目横跨美国三所主要大学和五个院系，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其他本科生组成的团队领导，他们将在顶级场馆、会议研讨会以及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，从少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重点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从根本上仅限于稀疏（且很少）的数据观察，因为大多数结构已被侵蚀或破坏。推理方法显示出巨大的前景，但它们在数据相对稀疏和结构模糊的情况下举步维艰。作为第一个领域应用程序，该团队将协助计算考古学家拥有相对稀疏的数据，但具有底层结构化站点。首先，他们将解决集合覆盖问题，以确定最小但足以跨越稀疏数据的离散原子元素和规则集。其次，他们将使用这些原子元素和规则来生成足够的数据样本，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示来对考古遗址进行建模，从而实现重建、语义理解和遗址分类等。此外，该团队预计开发的模型可以重新设计，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 NSF 的法定使命，并通过使用基金会的智力价值进行评估，被认为值得支持以及更广泛的影响审查标准。

项目成果

Guided pluralistic building contour completion

引导多元建筑轮廓完成

• DOI: 10.1007/s00371-022-02532-z
• 发表时间: 2022-09
• 期刊: The Visual Computer
• 作者: Zhang, Xiaowei;Ma, Wufei;Varinlioglu, Gunder;Rauh, Nick;He, Liu;Aliaga, Daniel
• 通讯作者: Aliaga, Daniel

Tree Instance Segmentation with Temporal Contour Graph

使用时间轮廓图进行树实例分割

• DOI: 10.1109/cvpr52729.2023.00218
• 发表时间: 2023-06
• 期刊: IEEE
• 作者: Firoze, Adnan;Wingren, Cameron;Yeh, Raymond A.;Benes, Bedrich;Aliaga, Daniel
• 通讯作者: Aliaga, Daniel

Generative Building Feature Estimation From Satellite Images

根据卫星图像生成建筑特征估计

• DOI: 10.1109/tgrs.2023.3242284
• 发表时间: 2024-09-13
• 期刊: IEEE Transactions on Geoscience and Remote Sensing
• 影响因子: 8.2
• 作者: Liu He;J. Shan;Daniel G. Aliaga
• 通讯作者: Daniel G. Aliaga

GlobalMapper: Arbitrary-Shaped Urban Layout Generation

GlobalMapper：任意形状的城市布局生成

• 发表时间: 2023-10
• 期刊: IEEE International Conference on Computer Vision
• 作者: He, Liu;Aliaga, Daniel
• 通讯作者: Aliaga, Daniel

Urban tree generator: spatio-temporal and generative deep learning for urban tree localization and modeling

城市树木生成器：用于城市树木定位和建模的时空和生成深度学习

• DOI: 10.1007/s00371-022-02526-x
• 发表时间: 2022-09
• 期刊: The Visual Computer
• 作者: Firoze, Adnan;Benes, Bedrich;Aliaga, Daniel
• 通讯作者: Aliaga, Daniel