WFDroneBench: A Benchmark for Sensor Placement and Drone Routing for Wildfire Detection
Abstract
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.
Related
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BibTeX
@misc{puech2026wfdronebench,
title = {{WFDroneBench: A Benchmark for Sensor Placement and Drone Routing for Wildfire Detection}},
author = {Romain Puech and Joseph Ye and Danique de Moor and Ana Trisovic and Dimitris Bertsimas},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.11829},
eprint = {2609.11829},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.11829},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - WFDroneBench: A Benchmark for Sensor Placement and Drone Routing for Wildfire Detection
AU - Romain Puech
AU - Joseph Ye
AU - Danique de Moor
AU - Ana Trisovic
AU - Dimitris Bertsimas
PY - 2026
DA - 2026/09/17
PB - arXiv
DO - 10.48550/arxiv.2609.11829
UR - https://arxiv.org/abs/2609.11829
AB - 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.
ER -