  Predicting socioeconomic indicators within urban regions is crucial for
fostering inclusivity, resilience, and sustainability in cities and human
settlements. While pioneering studies have attempted to leverage multi-modal
data for socioeconomic prediction, jointly exploring their underlying semantics
remains a significant challenge. To address the gap, this paper introduces a
Multi-Semantic Contrastive Learning (MuseCL) framework for fine-grained urban
region profiling and socioeconomic prediction. Within this framework, we
initiate the process by constructing contrastive sample pairs for street view
and remote sensing images, capitalizing on the similarities in human mobility
and Point of Interest (POI) distribution to derive semantic features from the
visual modality. Additionally, we extract semantic insights from POI texts
embedded within these regions, employing a pre-trained text encoder. To merge
the acquired visual and textual features, we devise an innovative
cross-modality-based attentional fusion module, which leverages a contrastive
mechanism for integration. Experimental results across multiple cities and
indicators consistently highlight the superiority of MuseCL, demonstrating an
average improvement of 10% in $R^2$ compared to various competitive baseline
models. The code of this work is publicly available at
https://github.com/XixianYong/MuseCL.
