Urban AI Lab
Big data and computational algorithms have gradually become integrated with the built environment and within human’s daily lives, leading to a significant rise in Urban Artificial Intelligence (AI) research and applications. As such, the Urban AI Lab will develop digital twins and virtual/augmented reality (VR/AR) for multi-scaled simulations and scenarios for cities and regions. Further, such a platform will facilitate a collective understanding of existing urban infrastructure conditions and demonstrate innovative capabilities for how to increase urban resilience and efficacy beyond the technology integration. The Urban AI Lab will develop and support free and open-source software tools for reproducible urban problem solving. We will provide contextualized modeling of cities to dynamically analyze real-time built environments and test existing and future scenarios for sustainable growth and climate action, involving eight founding members and 48 other members across nine colleges at TAMU, including Galveston campus, TEES, AgriLife Research, and TTI. We also aim to make the urban data FAIR (Findable, Accessible, Interoperable, and Reusable) through an AI-based data sharing platform.
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Built env. talk series: Dr. Qian He (June 1st)
Title: From Sea Level Rise to Presidential Declared Disasters: AddressingClimate Resilience and Social Justice slides: recording:
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Towards Ethical Geo-Design in the Urban Digital Twin Era: Opportunities and Challenges
An Open Call for Submissions: Special Issue of the Journal of Planning Education and Research (JPER) Aims: The urban digital twin (UDT) is a nascent technology that results from the […]
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Built env. talk series: Zijie Huang (May 18th)
Coupled Graph ODE for Learning Interacting System Dynamics Summary Many real-world systems such as social networks and moving planets are dynamic in nature, where a set of coupled objects are […]
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Built env. talk series: Tyler D. Hoffman (May 11th)
Controlling for spatial confounding and spatial interference in causal inference: Modeling insights and the spycause package Summary Causal inference is a rapidly growing field of statistics that applies logical reasoning […]
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Built env. talk series: Dr. Gengchen Mai (May 25th)
On the Opportunities and Challenges of Foundation Models for Geospatial Artificial Intelligence Summary Large pre-trained models, also known as foundation models (FMs), are trained in a task-agnostic manner on large-scale […]
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Built env. talk series: Dr. Takahiro Yabe (May 4th)
Decreased income diversity of urban encounters during the pandemic Summary Diversity of physical encounters in urban environments is known to spur economic productivity while also fostering social capital. However, mobility […]
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Built env. talk series: Dr. Tianbao Yang
Representation Learning for Generative AI Dr. Tianbao Yang Associate Professor Computer Science & Engineering Texas A&M University Dr. Tianbao Yang is an Associate Professor at CSE department of Texas A&M […]
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Digital Twins, Digital Earth, and Ethics by Dr. Michael Goodchild
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Built env. talk series: Dr. Jinmeng Rao
Privacy-Preserving Location Recommendation via Decentralized Collaborative Learning Summary The widespread use of mobile Internet infrastructure in urban areas and the proliferation of location-aware mobile devices have led to a significant […]
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Built env. talk series: Wataru Morioka
Spatially-weighted Network Dual K Function: An Extended Statistical Method for Analyzing Co-location on a Street Network Summary Capturing spatial co-location patterns—subsets of two or more groups of events that are […]