1 citations · 1 across the 4 of their papers we have counts for
6 papers
CoST: Semantic-Aware Urban Understanding via Spatial-Temporal Alignment
Yutian Jiang, Jiabo Liu, Xixuan Hao +1
Geospatial representation learning from satellite imagery is a fundamental problem for large-scale urban analysis and real-world applications. Despite recent advances, current meth…
Multi-Agent Collaborative Reasoning with Tool-Augmented Evidence for Urban Region Profiling
Xixuan Hao, Yutian Jiang, Jiabo Liu +4
Urban region profiling constitutes a core problem in urban computing, supporting applications such as population estimation, economic assessment, and environmental monitoring. Exis…
AgentSense: LLMs Empower Generalizable and Explainable Web-Based Participatory Urban Sensing
Xusen Guo, Mingxing Peng, Xixuan Hao +4
Web-based participatory urban sensing has emerged as a vital approach for modern urban management by leveraging mobile individuals as distributed sensors. However, existing urban s…
Traffic-R1: Reinforced LLMs Bring Human-Like Reasoning to Traffic Signal Control Systems
Xingchen Zou, Yuhao Yang, Zheng Chen +4
We introduce Traffic-R1, a 3B-parameter foundation model with human-like reasoning for Traffic signal control (TSC), developed via self-exploration and iterative reinforcement of L…
Geospatial Representation Learning: A Survey from Deep Learning to The LLM Era
Xixuan Hao, Yutian Jiang, Xingchen Zou +6
The ability to transform location-centric geospatial data into meaningful computational representations has become fundamental to modern spatial analysis and decision-making. Geosp…
GraphAgent: Agentic Graph Language Assistant
Yuhao Yang, Jiabin Tang, Lianghao Xia +3
Real-world data is represented in both structured (e.g., graph connections) and unstructured (e.g., textual, visual information) formats, encompassing complex relationships that in…