CityRiSE: Reasoning Urban Socio-Economic Status in Vision-Language
  Models via Reinforcement Learning
Harnessing publicly available, large-scale web data, such as street view and
satellite imagery, urban socio-economic sensing is of paramount importance for
achieving global sustainable development goals. With the emergence of Large
Vision-Language Models (LVLMs), new opportunities have arisen to solve this
task by treating it as a multi-modal perception and understanding problem.
However, recent studies reveal that LVLMs still struggle with accurate and
interpretable socio-economic predictions from visual data. To address these
limitations and maximize the potential of LVLMs, we introduce
CityRiSE, a novel framework for Reasoning urban
Socio-Economic status in LVLMs through pure reinforcement
learning (RL). With carefully curated multi-modal data and verifiable reward
design, our approach guides the LVLM to focus on semantically meaningful visual
cues, enabling structured and goal-oriented reasoning for generalist
socio-economic status prediction. Experiments demonstrate that CityRiSE with
emergent reasoning process significantly outperforms existing baselines,
improving both prediction accuracy and generalization across diverse urban
contexts, particularly for prediction on unseen cities and unseen indicators.
This work highlights the promise of combining RL and LVLMs for interpretable
and generalist urban socio-economic sensing.
69044d8c2c556835fa67f13a
Tianhui Liu
69044d8c2c556835fa67f13b
Hetian Pang
69044d8c2c556835fa67f13c
Xin Zhang
69044d8c2c556835fa67f13d
Jie Feng
claimed_verified
2025-10-31T14:26:32.088Z
6465d3bd63e7e09dd02e95c3
/avatars/b2798bd5f8368f956bf7fab79d9432f0.svg
Jie Feng
JJ-TMT
user
JJ-TMT
69044d8c2c556835fa67f13e
Yong Li
69044d8c2c556835fa67f13f
Pan Hui
CityRiSE: Reasoning Urban Socio-Economic Status in Vision-Language
  Models via Reinforcement Learning
Harnessing publicly available, large-scale web data, such as street view and
satellite imagery, urban socio-economic sensing is of paramount importance for
achieving global sustainable development goals. With the emergence of Large
Vision-Language Models (LVLMs), new opportunities have arisen to solve this
task by treating it as a multi-modal perception and understanding problem.
However, recent studies reveal that LVLMs still struggle with accurate and
interpretable socio-economic predictions from visual data. To address these
limitations and maximize the potential of LVLMs, we introduce
CityRiSE, a novel framework for Reasoning urban
Socio-Economic status in LVLMs through pure reinforcement
learning (RL). With carefully curated multi-modal data and verifiable reward
design, our approach guides the LVLM to focus on semantically meaningful visual
cues, enabling structured and goal-oriented reasoning for generalist
socio-economic status prediction. Experiments demonstrate that CityRiSE with
emergent reasoning process significantly outperforms existing baselines,
improving both prediction accuracy and generalization across diverse urban
contexts, particularly for prediction on unseen cities and unseen indicators.
This work highlights the promise of combining RL and LVLMs for interpretable
and generalist urban socio-economic sensing.
CityRiSE, a reinforcement learning framework, enhances Large Vision-Language Models for accurate and interpretable urban socio-economic status prediction using multi-modal data.
Large Vision-Language Models LVLMs reinforcement learning RL multi-modal data socio-economic status prediction urban socio-economic sensing semantically meaningful visual cues structured reasoning goal-oriented reasoning prediction accuracy generalization