CityRiSE: Reasoning Urban Socio-Economic Status in Vision-Language Models via Reinforcement Learning

TL;DR

CityRiSE leverages reinforcement learning to enhance vision-language models for accurate, interpretable, and generalizable urban socio-economic status prediction using multi-modal visual data.

Contribution

This paper introduces CityRiSE, a reinforcement learning framework that guides LVLMs to focus on meaningful visual cues for socio-economic prediction, improving accuracy and interpretability.

Findings

Outperforms existing baselines in accuracy and generalization

Enables reasoning on unseen cities and indicators

Demonstrates the effectiveness of RL in guiding LVLMs

Abstract

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 \textbf{CityRiSE}, a novel framework for \textbf{R}eason\textbf{i}ng urban \textbf{S}ocio-\textbf{E}conomic 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.