WFDroneBench：用于野火检测的传感器布设与无人机路径规划基准
WFDroneBench: A Benchmark for Sensor Placement and Drone Routing for Wildfire Detection
AI总结：
提出WFDroneBench基准库，集成风险图与优化策略，在7746个场景中验证风险感知的Max-Coverage策略显著提升野火检测速度，并揭示小火灾检测与风险图预测两大挑战。
AI中文摘要：
日益频繁和严重的野火威胁着生态系统、公共健康和基础设施。早期检测至关重要，但现有监测系统能力有限。无人机提供移动、实时的覆盖，但在动态火区中优化传感器布设和无人机路径规划仍然具有挑战性。为解决这一问题，我们提出WFDroneBench，一个用于早期野火检测的开源Python基准测试库，它将机器学习风险图与基于优化的传感器、充电站和无人机部署策略相结合。它使用标准化指标和真实野火模拟来评估风险图、优化策略和监测设备。该框架支持跨预测和决策组件的基准测试：机器学习研究人员可以评估风险模型并比较路径规划策略。WFDroneBench包含49个地点的7746个场景，基于历史火点、真实野火风险图和模拟火势蔓延构建，并包含两种地面探测器和三种无人机路径规划策略。我们的实验表明，当风险图足够准确时，风险感知策略Max-Coverage显著优于其他基线，在最困难的火情上实现了最快检测。我们进一步发现，即使在风险图不完美的情况下，风险感知的静态基础设施也有帮助，且基于无人机的检测优于地面传感器。最后，我们的结果揭示了两个关键开放挑战：（i）快速可靠地检测小规模火灾，以及（ii）改进风险图预测，其中真实火点模式与可用风险图之间的差距凸显了机器学习创新的重要机遇。我们公开发布所有代码、数据和文档。
英文摘要：
Increasingly frequent and severe wildfires threaten ecosystems, public health, and infrastructure. Early detection is vital but limited by existing monitoring systems. Drones offer mobile, real-time coverage, but optimizing sensor placement and drone routing in dynamic fire zones remains challenging. To address this, we introduce WFDroneBench, an open-source Python benchmarking library for early wildfire detection that integrates machine-learned risk maps with optimization-based deployment strategies for sensors, charging stations, and drones. It evaluates risk maps, optimization strategies, and monitoring equipment using standardized metrics and realistic wildfire simulations. The framework supports benchmarking across predictive and decision-making components: machine learning researchers can assess risk models and compare routing strategies. WFDroneBench includes 7746 scenarios across 49 locations, built from historical ignitions, real-world wildfire risk maps, and simulated fire spread, along with two ground detector and three drone routing strategies. Our experiments show that the risk-aware strategy Max-Coverage significantly outperforms other baselines when risk maps are sufficiently accurate, achieving the fastest detection on the most difficult fires. We further find that risk-aware static infrastructure helps even under an imperfect risk map and drone-based detection outperforms ground sensors. Finally, our results reveal two key open challenges: (i) detecting small fires rapidly and reliably, and (ii) improving risk-map prediction, where the gap between ground-truth ignition patterns and available risk maps highlights a significant opportunity for ML innovation. We openly release all code, data, and documentation.
