Rapid drone-based wildfire detection at a fraction of current prevention spending

2026-09-16

AI Summary

研究通过优化无人机监控网络布局和路径，以较低成本实现快速大规模野火检测，显著提高检测效率和速度。

Abstract

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.

Problem

• wildfire detection
• monitoring infrastructure
• detection speed
• spatial coverage
• cost-effectiveness

Innovation

• drone-based monitoring
• infrastructure placement optimization
• autonomous drone routing
• cost-effectiveness
• wildfire detection speed