# FIRE-VLM: A Vision-Language-Driven Reinforcement Learning Framework for UAV Wildfire Tracking in a Physics-Grounded Fire Digital Twin

arXiv: https://arxiv.org/abs/2601.03449 · HTML: https://mlsanity.com/paper/2601.03449/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jan 2026.

## Built on by (most cited first, 2 of 2)

- [WildFireVQA: A Large-Scale Radiometric Thermal VQA Benchmark for Aerial Wildfire Monitoring](https://mlsanity.com/paper/2604.20190/) (2026)
- [A Simulation-Grounded Agentic VLM Framework for Wildfire Monitoring and Reporting](https://mlsanity.com/paper/2610.02451/) (2026)

## Abstract (arXiv)

Wildfire monitoring demands autonomous systems capable of reasoning under extreme visual degradation, rapidly evolving physical dynamics, and scarce real-world training data. Existing UAV navigation approaches rely on simplified simulators and supervised perception pipelines, and lack embodied agents interacting with physically realistic fire environments. We introduce FIRE-VLM, the first end-to-end vision-language model (VLM) guided reinforcement learning (RL) framework trained entirely within a high-fidelity, physics-grounded wildfire digital twin. Built from USGS Digital Elevation Model (DEM) terrain, LANDFIRE fuel inventories, and semi-physical fire-spread solvers, this twin captures terrain-induced runs, wind-driven acceleration, smoke plume occlusion, and dynamic fuel consumption. Within this environment, a PPO agent with dual-view UAV sensing is guided by a CLIP-style VLM. Wildfire-specific semantic alignment scores, derived from a single prompt describing active fire and smoke plumes, are integrated as potential-based reward shaping signals. Our contributions are: (1) a GIS-to-simulation pipeline for constructing wildfire digital twins; (2) a VLM-guided RL agent for UAV firefront tracking; and (3) a wildfire-aware reward design that combines physical terms with VLM semantics. Across five digital-twin evaluation tasks, our VLM-guided policy reduces time-to-detection by up to 6 times, increases time-in-FOV, and is, to our knowledge, the first RL-based UAV wildfire monitoring system demonstrated in kilometer-scale, physics-grounded digital-twin fires.