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[2507.10134] FRSICL: LLM-Enabled In-Context Learning Flight Resource Allocation for Fresh Data Collection in UAV-Assisted Wildfire Monitoring
Summary
The paper presents FRSICL, a novel method utilizing LLMs for optimizing UAV data collection in wildfire monitoring, enhancing efficiency and decision-making in real-time.
Why It Matters
As wildfires pose significant environmental threats, efficient monitoring is crucial. FRSICL leverages advanced AI techniques to improve data collection strategies, potentially leading to faster response times and better resource allocation in critical situations.
Key Takeaways
- FRSICL optimizes UAV flight paths for wildfire monitoring using LLMs.
- In-Context Learning allows real-time decision-making without extensive retraining.
- Simulation results show FRSICL outperforms traditional methods like DRL.
- The approach minimizes the Age of Information (AoI) for better data relevance.
- This innovation could significantly enhance public safety measures in wildfire management.
Computer Science > Artificial Intelligence arXiv:2507.10134 (cs) [Submitted on 14 Jul 2025 (v1), last revised 17 Feb 2026 (this version, v2)] Title:FRSICL: LLM-Enabled In-Context Learning Flight Resource Allocation for Fresh Data Collection in UAV-Assisted Wildfire Monitoring Authors:Yousef Emami, Hao Zhou, Miguel Gutierrez Gaitan, Kai Li, Luis Almeida View a PDF of the paper titled FRSICL: LLM-Enabled In-Context Learning Flight Resource Allocation for Fresh Data Collection in UAV-Assisted Wildfire Monitoring, by Yousef Emami and 4 other authors View PDF HTML (experimental) Abstract:Uncrewed Aerial Vehicles (UAVs) play a vital role in public safety, especially in monitoring wildfires, where early detection reduces environmental impact. In UAV-Assisted Wildfire Monitoring (UAWM) systems, jointly optimizing the data collection schedule and UAV velocity is essential to minimize the average Age of Information (AoI) for sensory data. Deep Reinforcement Learning (DRL) has been used for this optimization, but its limitations-including low sampling efficiency, discrepancies between simulation and real-world conditions, and complex training make it unsuitable for time-critical applications such as wildfire monitoring. Recent advances in Large Language Models (LLMs) provide a promising alternative. With strong reasoning and generalization capabilities, LLMs can adapt to new tasks through In-Context Learning (ICL), which enables task adaptation using natural language prompts and exam...
