CityRiSE: Reasoning Urban Socio-Economic Status in Vision-Language
  Models via Reinforcement Learning
Authors: name: Tianhui Liu, name: Hetian Pang, name: Xin Zhang, name: Jie Feng, name: Yong Li, name: Pan Hui

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.