140 citations · 328 across the 38 of their papers we have counts for
43 papers
USAI-Quant: A Quantitative Reasoning Benchmark for Vision-Language Models in Built Environments
Dongdong Wang, Qingqi Song, Yuzhou Chen +3
Large vision-language models (VLMs) have emerged as a powerful paradigm for urban and spatial AI. However, current state-of-the-art large VLMs still struggle with quantitative reas…
Gradient-Guided Decoupled Adaptation for Geospatial Vision-Language Models
Dongdong Wang, Deepak Balakrishnan, Ravi Srinivasan +1
Existing geospatial vision-language models (Geo-VLMs) typically optimize diverse geospatial tasks through a unified multi-task adaptation paradigm without explicitly accounting for…
Plan-to-Synthesis: Cross-City Human Mobility Generation via Semantic Latent Flow Matching
Zhoufu Wang, Baoshen Guo, Zhiqing Hong +6
Human mobility generation aims to synthesize realistic point-of-interest (POI) visitation trajectories and has become an important tool for travel behavior modeling, transportation…
BASeg: Boundary-Aware Remote Sensing Segmentation with Structural Penalties
Yuexi Song, Kailai Sun, Zhuoyu Wang +4
Semantic segmentation is a core computer vision task in the remote sensing field, accelerating advancements in ur- ban development, agriculture, ecology, water resources, and envir…
Public transit gains and spatially uneven travel demand changes after NYC congestion pricing
Donghang Li, Dingyi Zhuang, Yunlin Li +5
New York City implemented the nation's first cordon-based congestion pricing program in January 2025, providing an opportunity to evaluate how system-wide urban mobility responds t…
Enhancing the Socioeconomic Understanding of Foundation Models with Urban Mobility
Baoshen Guo, Donghang Li, Zhiqing Hong +4
Foundation models have recently been applied to urban socioeconomic prediction using POI text, satellite imagery, and geospatial descriptions. However, these models mostly rely on…
SENSE: Satellite-based ENergy Synthesis for Sustainable Environment
Kailai Sun, Mingyi He, Heye Huang +5
Urban Building Energy Modeling plays a critical role in achieving the United Nations' Sustainable Development Goals 7 and 11. Although existing studies based on satellite imagery a…
Designing streetscapes from street-view imagery using diffusion models
Yuzhou Chen, Yuebing Liang, Lingqian Hu +4
Street-view imagery (SVI) is widely used to quantify key indicators of urban environment, such as green- ery, sky, or road view indices. However, existing studies largely focus on…
Built Environment Reasoning from Remote Sensing Imagery Using Large Vision--Language Models
Dongdong Wang, Deepak Balakrishnan, Ravi Srinivasan +1
This work investigates the use of large language models (LLMs) for tasks in smart cities. The core idea is to leverage remote sensing imagery to characterize the built environment,…
Envisioning global urban development with satellite imagery and generative AI
Kailai Sun, Yuebing Liang, Mingyi He +5
Urban development has been a defining force in human history, shaping cities for centuries. However, past studies mostly analyze such development as predictive tasks, failing to re…
TrustEnergy: A Unified Framework for Accurate and Reliable User-level Energy Usage Prediction
Dahai Yu, Rongchao Xu, Dingyi Zhuang +3
Energy usage prediction is important for various real-world applications, including grid management, infrastructure planning, and disaster response. Although a plethora of deep lea…
RAST-MoE-RL: A Regime-Aware Spatio-Temporal MoE Framework for Deep Reinforcement Learning in Ride-Hailing
Yuhan Tang, Kangxin Cui, Jung Ho Park +6
Ride-hailing platforms face the challenge of balancing passenger waiting times with overall system efficiency under highly uncertain supply-demand conditions. Adaptive delayed matc…
AlphaOPT: Formulating Optimization Programs with Self-Improving LLM Experience Library
Minwei Kong, Ao Qu, Xiaotong Guo +12
Optimization modeling underlies critical decision-making across industries, yet remains difficult to automate: natural-language problem descriptions must be translated into precise…
Alternative Graph Neural Networks: Synergizing GEV Models and Deep Learning for Travel Mode Choice Modeling
Yuqi Zhou, Zhanhong Cheng, Dingyi Zhuang +3
Generalized extreme value models capture dependence among choice alternatives in discrete choice modeling, but require this dependence to be predefined, symmetric, and shared unifo…
UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction
Dahai Yu, Dingyi Zhuang, Lin Jiang +5
Spatiotemporal prediction plays a critical role in numerous real-world applications such as urban planning, transportation optimization, disaster response, and pandemic control. In…
Graph neural networks for residential location choice: connection to classical logit models
Zhanhong Cheng, Lingqian Hu, Yuheng Bu +2
Researchers have adopted deep learning for classical discrete choice analysis as it can capture complex feature relationships and achieve higher predictive performance. However, th…
Leveraging the Spatial Hierarchy: Coarse-to-fine Trajectory Generation via Cascaded Hybrid Diffusion
Baoshen Guo, Zhiqing Hong, Junyi Li +2
Urban mobility data has significant connections with economic growth and plays an essential role in various smart-city applications. However, due to privacy concerns and substantia…
Generative AI for Urban Design: A Stepwise Approach Integrating Human Expertise with Multimodal Diffusion Models
Mingyi He, Yuebing Liang, Shenhao Wang +5
Urban design is a multifaceted process that demands careful consideration of site-specific constraints and collaboration among diverse professionals and stakeholders. The advent of…
Generative AI for Urban Planning: Synthesizing Satellite Imagery via Diffusion Models
Qingyi Wang, Yuebing Liang, Yunhan Zheng +3
Generative AI offers new opportunities for automating urban planning by creating site-specific urban layouts and enabling flexible design exploration. However, existing approaches…
Analyzing sequential activity and travel decisions with interpretable deep inverse reinforcement learning
Yuebing Liang, Shenhao Wang, Jiangbo Yu +3
Travel demand modeling has shifted from aggregated trip-based models to behavior-oriented activity-based models because daily trips are essentially driven by human activities. To a…
Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study
Dingyi Zhuang, Hanyong Xu, Xiaotong Guo +3
Urban prediction tasks, such as forecasting traffic flow, temperature, and crime rates, are crucial for efficient urban planning and management. However, existing Spatiotemporal Gr…
Virtual Nodes Improve Long-term Traffic Prediction
Xiaoyang Cao, Dingyi Zhuang, Jinhua Zhao +1
Effective traffic prediction is a cornerstone of intelligent transportation systems, enabling precise forecasts of traffic flow, speed, and congestion. While traditional spatio-tem…
Sparkle: Mastering Basic Spatial Capabilities in Vision Language Models Elicits Generalization to Spatial Reasoning
Yihong Tang, Ao Qu, Zhaokai Wang +7
Vision language models (VLMs) perform well on many tasks but often fail at spatial reasoning, which is essential for navigation and interaction with physical environments. Many spa…
GETS: Ensemble Temperature Scaling for Calibration in Graph Neural Networks
Dingyi Zhuang, Chonghe Jiang, Yunhan Zheng +2
Graph Neural Networks deliver strong classification results but often suffer from poor calibration performance, leading to overconfidence or underconfidence. This is particularly p…