On Multimodal Large Model-Driven Generation of Forest Fire Situation Quick Report

In the process of forest fire suppression, the conditions at the fireground can be complex and rapidly changing, it is crucial to obtain situational intelligence from the fireground as quickly as possible to assist firefighters in ensuring their safety while formulating targeted firefighting strategies. However, in large-scale fireground where terrain information is unclear, relying on manual fire detection is not only time-consuming and labor-intensive but also highly dangerous. Additionally, fire situation reports generated from satellite remote sensing imaging and analysis often lack detailed or accurate information for specific ground areas and suffer from insufficient timeliness. Even when integrating multiple sources of data collected from the fireground to generate fire situation reports, there are still issues with simple data stacking and insufficient semantic information correlation. To address these challenges, this paper proposes a multi-modal large model-driven forest fire situation quick report generation technology based on UAVs detection. The method leverages UAVs for comprehensive detection along the fire perimeter, while the digital battlefield command and control platform facilitates three-dimensional reconstruction of the terrain and captures orthophoto image. The orthophoto image combined with temperature data from fireground sensors and third-party meteorological information are transmitted to a ground AI fusion gateway. The forest fire situation quick report generation agent, driven by a multimodal large model, operates through three steps: collaborative recognition using a large vision model and specialized object detection model, optimal path planning based on a large language model and specialized segmentation model, and the generation of the forest fire situation quick report. This approach achieves rapid and accurate acquisition of forest fire situation intelligence. Experimental results show that the proposed method is more efficient and safer compared to traditional manual fire detection; it provides more detailed local area information and higher timeliness compared to fire intelligence gathered from satellite remote sensing; and the use of multi-modal large model technology for multi-source data fusion between ground and aerial collection results in a more intuitive and focused forest fire situation quick report. This marks an important step toward reducing firefighter casualties and improving overall firefighting efficiency.