Abstract
Effective wildfire monitoring requires relating visual evidence to physical fire dynamics, yet real videos with synchronized physical annotations are scarce and high-fidelity 3D simulation is costly. We present a simulation-grounded vision-language model (VLM) framework that automatically converts 2D wildfire simulations into labeled video episodes. A fixed Blender mapping produces low-detail 3D proxies aligned with simulator terrain, fuel layout, fire activity, and wind cues; controllable video generation supplies richer appearance. The proxies are intermediate representations rather than finely rendered final scenes. Generated videos and simulator labels form reusable multimodal memory for a training-free multi-agent VLM system that retrieves reference episodes, reconciles visual and memory-based predictions, and produces structured wildfire reports. On held-out generated episodes, video memory achieves 51.5% exact four-tag accuracy, compared with 22.6% for direct VLM querying and 16-17% for text-only memory; the complete system achieves 77.3% accuracy on six simulator-derived report fields. Component ablations, cross-generator tests, and three real-UAV evaluations assess retrieval, reporting, generator changes, and observable monitoring tasks. The framework connects automatic simulation-to-proxy conversion with memory-based VLM reasoning under scarce real-world physical annotations.
Keywords
Subject
Publication details
- Journal
- Not available
- Open access
- Green open access
Cite this article
APA 7
Chen, D., Sun, Y., Li, Z., Liao, Y., Wang, S., Waanders, B. V. B., & Zhu, B. (2026). A Simulation-Grounded Agentic VLM Framework for Wildfire Monitoring and Reporting. https://omanscience.com/en/articles/a-simulation-grounded-agentic-vlm-framework-for-wildfire-monitoring-and-reporting
MLA 9
Chen, Duowen, et al. "A Simulation-Grounded Agentic VLM Framework for Wildfire Monitoring and Reporting." https://omanscience.com/en/articles/a-simulation-grounded-agentic-vlm-framework-for-wildfire-monitoring-and-reporting.
Chicago (author–date)
Chen, Duowen, Yuchen Sun, Zhiqi Li, Yuxuan Liao, Sinan Wang, Bart van Bloemen Waanders, and Bo Zhu. 2026. "A Simulation-Grounded Agentic VLM Framework for Wildfire Monitoring and Reporting." https://omanscience.com/en/articles/a-simulation-grounded-agentic-vlm-framework-for-wildfire-monitoring-and-reporting.
Harvard
Chen, D., Sun, Y., Li, Z., Liao, Y., Wang, S., Waanders, B. V. B. and Zhu, B. (2026) 'A Simulation-Grounded Agentic VLM Framework for Wildfire Monitoring and Reporting', Available at: https://omanscience.com/en/articles/a-simulation-grounded-agentic-vlm-framework-for-wildfire-monitoring-and-reporting.
Vancouver
Chen D, Sun Y, Li Z, Liao Y, Wang S, Waanders BVB, et al. A Simulation-Grounded Agentic VLM Framework for Wildfire Monitoring and Reporting. https://omanscience.com/en/articles/a-simulation-grounded-agentic-vlm-framework-for-wildfire-monitoring-and-reporting
IEEE
D. Chen, Y. Sun, Z. Li, Y. Liao, S. Wang, B. V. B. Waanders, and B. Zhu, "A Simulation-Grounded Agentic VLM Framework for Wildfire Monitoring and Reporting," https://omanscience.com/en/articles/a-simulation-grounded-agentic-vlm-framework-for-wildfire-monitoring-and-reporting.