Post-hurricane building damage assessment using street-view imagery and structured data: A multi-modal deep learning approach

Understand

Accurately assessing building damage is critical for disaster response and recovery.

• However, many existing models for detecting building damage have poor prediction accuracy due to their limited capabilities of identifying detailed, comprehensive structural and/or non-structural damage from the street-view image.
• Additionally, these models mainly rely on the imagery data for damage classification, failing to account for other critical information, such as wind speed, building characteristics, evacuation zones, and distance of the building to the hurricane track.
• To address these limitations, in this study, we propose a novel multi-modal (i.e., imagery and structured data) approach for post-hurricane building damage classification, named the Multi-Modal Swin Transformer (MMST).