CityLens: Benchmarking Large Language-Vision Models for Urban Socioeconomic Sensing

Tianhui Liu, Jie Feng, Hetian Pang, Xin Zhang, TIANJIAN OUYANG, Zhiyuan Zhang, Yong Li

CityLens: Benchmarking Large Language-Vision Models for Urban Socioeconomic Sensing: 2 upvotes on Hugging Face Daily Papers, #51 of 62 papers on 2025-06-03. Day-by-day upvote history.

Understanding urban socioeconomic conditions through visual data is a challenging yet essential task for sustainable urban development and policy planning. In this work, we introduce CityLens, a comprehensive benchmark designed to evaluate the capabilities of large language-vision models (LLVMs) in predicting socioeconomic indicators from satellite and street view imagery. We construct a multi-modal dataset covering a total of 17 globally distributed cities, spanning 6 key domains: economy, education, crime, transport, health, and environment, reflecting the multifaceted nature of urban life. Based on this dataset, we define 11 prediction tasks and utilize three evaluation paradigms: Direct Metric Prediction, Normalized Metric Estimation, and Feature-Based Regression. We benchmark 17 state-of-the-art LLVMs across these tasks. Our results reveal that while LLVMs demonstrate promising perceptual and reasoning capabilities, they still exhibit limitations in predicting urban socioeconomic indicators. CityLens provides a unified framework for diagnosing these limitations and guiding future efforts in using LLVMs to understand and predict urban socioeconomic patterns. Our codes and datasets are open-sourced via https://github.com/tsinghua-fib-lab/CityLens.

Paper page on Hugging Face · arXiv

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