CityLens: Benchmarking Large Language-Vision Models for Urban
  Socioeconomic Sensing
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.
683ee127345bccde696b3969
Tianhui Liu
683ee127345bccde696b396a
Jie Feng
claimed_verified
2025-06-03T12:33:11.636Z
6465d3bd63e7e09dd02e95c3
/avatars/b2798bd5f8368f956bf7fab79d9432f0.svg
Jie Feng
user
JJ-TMT
683ee127345bccde696b396b
Hetian Pang
683ee127345bccde696b396c
Xin Zhang
683ee127345bccde696b396d
Tianjian Ouyang
admin_assigned
2025-06-03T12:43:35.219Z
6566fcb5118497d0af91dc3b
/avatars/e081d7d1b1657245cd818e5417cdcb2e.svg
TIANJIAN OUYANG
user
Ouyangtj
683ee127345bccde696b396e
Zhiyuan Zhang
683ee127345bccde696b396f
Yong Li
CityLens: Benchmarking Large Language-Vision Models for Urban
  Socioeconomic Sensing
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.
CityLens evaluates large language-vision models' capabilities in predicting urban socioeconomic indicators using multi-modal datasets from various global cities.
CityLens large language-vision models LLVMs satellite imagery street view imagery multi-modal dataset socioeconomic indicators Direct Metric Prediction Normalized Metric Estimation Feature-Based Regression perceptual capabilities reasoning capabilities urban socioeconomic patterns