SeismoMind: A zero-shot decision-tree based multimodal large language model framework for post-earthquake infrastructure damage understanding

Rapid assessment of post-earthquake infrastructure damage is essential for effective emergency response, yet current workflows remain time-consuming and labor intensive. Ground-level imagery can provide faster situational awareness than aerial or satellite observations, but its practical use is often constrained by the extensive annotation required by conventional supervised deep learning models. This study introduces SeismoMind, a decision-tree-based multimodal large language model (MLLM) framework for zero-shot post-earthquake damage understanding. Unlike conventional MLLM prompt development, which is typically guided by expert intuition, SeismoMind is informed by cognitive observations of non-experts and encodes a structured reasoning process toward expert-level damage assessment. This non-expert learning trajectory is encoded as a Decision-Tree Chain of Thought (DT-CoT) that guides the MLLM through a transparent, stepwise evaluation of infrastructure damage. SeismoMind is evaluated on two ground-view benchmarks. The Earthquake Image Database (EID) contains 13,513 online social media platforms. The 2023 Türkiye Earthquake dataset includes 675 ground-level field images collected during post-earthquake reconnaissance by the Geotechnical Extreme Event Reconnaissance (GEER) and the Earthquake Engineering Research Institute (EERI) team. The framework achieves accuracies of 90.53% and 80.60%, respectively, which are comparable to state-of-the-art supervised computer vision baselines (91.71% and 80.59%) while requiring no task-specific training. In a size-matched limited-data setting, SeismoMind further outperforms supervised baselines by 8.83%. These findings show that encoding structured, learnable reasoning patterns observed in non-experts provide a practical path for generative AI systems to perform post-disaster damage assessment from ground-view imagery without relying on large, expensive annotated datasets.