An explainable and transferable deep learning framework for spatiotemporal urban flood prediction by integrating Vision Transformer and U-Net | Synapse
March 3, 2026
An explainable and transferable deep learning framework for spatiotemporal urban flood prediction by integrating Vision Transformer and U-Net
Key Points
Accurate urban flood prediction provides critical insights for planning and response efforts, enhancing resilience to climate impacts.
Results indicate a significant increase in predictive performance over traditional models, crucial for timely interventions.
Framework integrates deep learning with advanced architectures—specifically Vision Transformer and U-Net—for robust analysis.
The approach supports adaptability, yet further validation in diverse urban settings is needed for broader applicability.