UrbanVLP: A Multi-Granularity Vision-Language Pre-Trained Foundation
  Model for Urban Indicator Prediction
Xixuan Hao Wei Chen Yibo Yan Siru Zhong Kun Wang Qingsong Wen Yuxuan Liang
Urban indicator prediction aims to infer socio-economic metrics in diverse
urban landscapes using data-driven methods. However, prevalent pre-trained
models, particularly those reliant on satellite imagery, face dual challenges.
Firstly, concentrating solely on macro-level patterns from satellite data may
introduce bias, lacking nuanced details at micro levels, such as architectural
details at a place. Secondly, the lack of interpretability in pre-trained
models limits their utility in providing transparent evidence for urban
planning. In response to these issues, we devise a novel Vision-Language
Pre-Trained Model (UrbanVLP) in this paper. Our UrbanVLP seamlessly integrates
multi-granularity information from both macro (satellite) and micro
(street-view) levels, overcoming the limitations of prior pre-trained models.
Moreover, it introduces automatic text generation and calibration, elevating
interpretability in downstream applications by producing high-quality text
descriptions of urban imagery. Rigorous experiments conducted across six
socio-economic tasks underscore UrbanVLP's superior performance. We also deploy
a web platform to verify its practicality.