6 papers
Mapping the City Through the Lens of Language Models
Wanqi Liu, Rong Zhao, Zhizhou Sha +2
Language models often complete an underspecified reference to a city with unstated assumptions about urban size, form, infrastructure, environment, and function. We measure those a…
LangRetrieval: Language-Guided Self-Evolving Satellite-to-Radar Retrieval via CSI-Driven Reward
Chunlei Shi, Junming Hou, Yi-Lin Wei +5
Satellite-to-radar (S2R) retrieval estimates ground radar precipitation from geostationary satellite observations, providing a critical solution for precipitation monitoring in rad…
Culturally uneven urban perception in large language models
Rong Zhao, Wanqi Liu, Zhizhou Sha +3
Large language models (LLMs) are increasingly used to describe and evaluate cities, yet the cultural structure of their urban judgments remains understudied. Here we introduce a me…
UrbanAlign: Post-hoc Semantic Calibration for VLM-Human Preference Alignment
Yecheng Zhang, Rong Zhao, Zhizhou Sha +10
Vision-language models (VLMs) can describe urban scenes in rich detail, yet consistently fail to produce reliable human preference labels in domain-specific tasks such as safety as…
WaveC2R: Wavelet-Driven Coarse-to-Refined Hierarchical Learning for Radar Retrieval
Chunlei Shi, Han Xu, Yinghao Li +4
Satellite-based radar retrieval methods are widely employed to fill coverage gaps in ground-based radar systems, especially in remote areas affected by terrain blockage and limited…
GenAI Models Capture Urban Science but Oversimplify Complexity
Yecheng Zhang, Rong Zhao, Zimu Huang +3
Generative artificial intelligence (GenAI) models are increasingly used for scientific data generation, yet their alignment with empirical knowledge in urban science remains unclea…