以当前预防支出的一小部分成本实现基于无人机的快速野火检测
Rapid drone-based wildfire detection at a fraction of current prevention spending
- Massachusetts Institute of Technology（麻省理工学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究联合优化监测设施布局与无人机航线，以1亿美元五年预算检测加州97.3%野火，成本仅为现有预防支出的5%，证明无人机监测更具成本效益。
AI中文摘要：
早期野火检测对于防止小火情升级为大规模灾害至关重要，然而当前的监测系统缺乏一个定量框架来在规模上配置检测基础设施。我们联合优化了监测基础设施的布局和自主无人机的航线规划，并考虑了现实运行约束，以量化实现快速、大规模野火检测所需的投资。基于2021年至2024年间加利福尼亚州3693起火灾的样本外评估，一个优化后的无人机网络在1亿美元五年预算下运行，可检测到97.3%的火灾，其中74%在首个一小时内被检测到。按五年摊销，该预算约为每年2000万美元，大约占加利福尼亚州年度野火预防支出的5%。在当前技术成本下，基于无人机的监测比静态地面传感器更具成本效益。检测能力主要受空间覆盖范围支配，而航线规划策略主要决定检测速度。
英文摘要：
Early wildfire detection is critical to prevent small ignitions from escalating into large-scale disasters, yet current monitoring systems lack a quantitative framework for allocating detection infrastructure at scale. We jointly optimize the placement of monitoring infrastructure and the routing of autonomous drones under realistic operational constraints to quantify the investment required for rapid, large-scale wildfire detection. Evaluated out-of-sample on 3,693 California ignitions from 2021-2024, an optimized drone network operating at a $100 million five-year budget detects 97.3% of fires, including 74% within the first hour. Amortized over five years, that budget is about $20 million per year, roughly 5% of California's annual wildfire-prevention expenditure. Under current technology costs, drone-based monitoring is substantially more cost-effective than static ground sensors. Detection is governed primarily by spatial coverage, while routing strategy mainly determines detection speed.
