  Urban region profiling aims to learn a low-dimensional representation of a
given urban area while preserving its characteristics, such as demographics,
infrastructure, and economic activities, for urban planning and development.
However, prevalent pretrained 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 pretrained models limits their utility in providing
transparent evidence for urban planning. In response to these issues, we devise
a novel framework entitled UrbanVLP based on Vision-Language Pretraining. Our
UrbanVLP seamlessly integrates multi-granularity information from both macro
(satellite) and micro (street-view) levels, overcoming the limitations of prior
pretrained 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 urban indicator prediction tasks underscore its superior
performance.
