Think First, Assign Next (ThiFAN-VQA): A Two-stage Chain-of-Thought Framework for Post-Disaster Damage Assessment

--Timely and accurate assessment of damages following natural disasters is essential for effective emergency response and recovery. Recent AI-based frameworks have been developed to analyze large volumes of aerial imagery collected by Unmanned Aerial V ehicles (UA Vs), providing actionable insights rapidly. However, creating and annotating data for training these models is costly and time-consuming, resulting in datasets that are limited in size and diversity. Furthermore, most existing approaches rely on traditional classification-based frameworks with fixed answer spaces, restricting their ability to provide new information without additional data collection or model retraining. Using pre-trained generative models built on in-context learning (ICL) allows for flexible and open-ended answer spaces. However, these models often generate hallucinated outputs or produce generic responses that lack domain-specific relevance. T o address these limitations, we propose Think First, Assign Next (ThiF AN-VQA), a two-stage reasoning-based framework for Visual Question Answering (VQA) in disaster scenarios. ThiF AN-VQA first generates structured reasoning traces using chain-of-thought (CoT) prompting and ICL to enable interpretable reasoning under limited supervision. A subsequent answer selection module evaluates the generated responses and assigns the most coherent and contextually accurate answer, effectively improve the model performance. Experiments on FloodNet and RescueNet-VQA, UA V-based datasets from flood-and hurricane-affected regions, demonstrate that ThiF AN-VQA achieves superior accuracy, interpretability, and adaptability for real-world post-disaster damage assessment tasks. N the immediate aftermath of natural disasters, first responders rely heavily on up-to-date information to assess damage, identify hazards, allocate resources, and reach survivors as quickly as possible.