FVE: Fire-Management-Aligned Vision–Language Reasoning for UAV-Based Fire-Event Assessment

Existing unmanned aerial vehicle fire monitoring methods primarily recognize visible smoke or flame cues but cannot reliably determine whether an observed anomaly is an uncontrolled fire requiring intervention. This study reformulates UAV fire monitoring from a conventional visual-recognition problem into an event-level reasoning and decision task. We propose Fire-Verify-Expert (FVE), a compact vision–language model that jointly performs smoke and fire cue confirmation, real-fire verification, event-type attribution, and ordered response-priority assessment. FVE incorporates fire-management logic into multimodal preference alignment through a multidimensional Group Relative Policy Optimization objective. Its reward mechanism constrains determination correctness, event-type consistency, rationale–decision consistency, and reasoning quality, while imposing asymmetric penalties on operationally costly errors, particularly missed real fires and severe underestimation of response priority. To support this task, we establish a UAV-oriented dataset of 600 images spanning varied environmental contexts and viewing perspectives, including real fires and visually similar controlled or currently non-actionable smoke/fire events. The dataset provides structured reasoning annotations and ordered response-priority labels. FVE achieves an accuracy/F1-score of 0.917/0.915 for real-fire verification, with a recall of 1.000 for real fires, and an accuracy/F1-score of 0.833/0.706 for response-priority assessment. These results demonstrate the effectiveness of FVE in aligning multimodal vision–language reasoning with fire-management requirements, providing a feasible approach for automated fire-event verification and response prioritization in frontline UAV fire-monitoring workflows.