Linking Data Modalities and Deep Learning Architectures for Urban Flood Modeling

Deep learning has been extensively applied to the modeling and management of urban flooding. While deep learning provides the analytical framework, the unique characteristics of the underlying data modalities themselves fundamentally drive model performance. Thus, this paper provides a scoping review of 289 recent studies (2016–2025), categorizing the relevant work into five modalities based on data acquisition methods, characteristics, and information processing workflows: Hydro-Geospatial Physical Base, Remote Sensing Imagery, Human Perception and Social Sensing, Environmental Acoustic, and Cross-Modal Integration. The hydro-geospatial physical base and remote sensing imagery dominate current studies because they are stable, widely available, and information-rich, whereas human behavior and acoustic data remain underutilized owing to heterogeneity, limited availability, and costly preprocessing. Key bottlenecks include information loss during preprocessing, insufficient cross-modal alignment, representations that struggle to balance statistical correlation with physical consistency, and marked regional variations in data. Ultimately, these limitations indicate that the field is still primarily dominated by purely data-driven models, and future progress requires integrating statistical learning with physical mechanisms to improve robustness and generalization capabilities.