Zero-Shot Vision-Language Model for Rapid Damaged Bridge Extraction in Emergency Response: A Case Study of the 2025 Myanmar Earthquake

In March 2025, a magnitude 7.9 earthquake struck Myanmar. Prompt and accurate identification of damaged Bridges is of great significance for emergency rescue and post-disaster reconstruction. Traditional methods' reliance on substantial sample data, coupled with spectral interference in complex scenarios, and complex algorithmic processes in scarce-sample situations, leads to recognition model construction difficulties and insufficient accuracy/efficiency for emergency response requirements. To enhance emergency response efficiency, this study proposes a rapid damage assessment framework for bridges integrating multi-source geospatial data with vision-language model. The framework incorporates a semantic prompt system for bridge damage assessment in complex disaster scenarios, embedding bridge condition features, typical damage patterns, and contextual descriptors. This system enables the vision-language model to autonomously extract structural damage features from imagery, achieving automated damage assessment directly from visual data. The proposed framework was implemented across 733 bridges within a 30km radius of the Myanmar earthquake epicenter. Experimental results demonstrate the method's capability to achieve precise damage localization and identification within critical post-disaster timeframes. For bridges beyond 30m in length, the system attained over 88% overall recognition accuracy with processing durations under four hours. This study represents the first implementation of vision-language models in emergency response protocols for infrastructure assessment without any samples for training, establishing an automated solution for post-disaster bridges (>30m) damage extraction. The framework demonstrates significant potential for practical implementation and scalability in disaster management operations.