  Monitoring sustainable development goals requires accurate and timely
socioeconomic statistics, while ubiquitous and frequently-updated urban imagery
in web like satellite/street view images has emerged as an important source for
socioeconomic prediction. Especially, recent studies turn to self-supervised
contrastive learning with manually designed similarity metrics for urban
imagery representation learning and further socioeconomic prediction, which
however suffers from effectiveness and robustness issues. To address such
issues, in this paper, we propose a Knowledge-infused Contrastive Learning
(KnowCL) model for urban imagery-based socioeconomic prediction. Specifically,
we firstly introduce knowledge graph (KG) to effectively model the urban
knowledge in spatiality, mobility, etc., and then build neural network based
encoders to learn representations of an urban image in associated semantic and
visual spaces, respectively. Finally, we design a cross-modality based
contrastive learning framework with a novel image-KG contrastive loss, which
maximizes the mutual information between semantic and visual representations
for knowledge infusion. Extensive experiments of applying the learnt visual
representations for socioeconomic prediction on three datasets demonstrate the
superior performance of KnowCL with over 30\% improvements on $R^2$ compared
with baselines. Especially, our proposed KnowCL model can apply to both
satellite and street imagery with both effectiveness and transferability
achieved, which provides insights into urban imagery-based socioeconomic
prediction.
