Towards reliable multimodal disaster severity assessment through preference optimization and explainable vision-language reasoning

Reliable disaster damage assessment requires models that provide both accurate predictions and transparent explanations. However, existing multimodal approaches are limited by scarce annotated data and insufficient evaluation of reasoning quality. This study proposes a two-stage training framework t