Rapid drone-based wildfire detection at a fraction of current prevention spending
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.
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BibTeX
@misc{puech2026rapid,
title = {{Rapid drone-based wildfire detection at a fraction of current prevention spending}},
author = {Romain Puech and Danique de Moor and Ana Trišović and Dimitris Bertsimas},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.18556},
eprint = {2609.18556},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.18556},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Rapid drone-based wildfire detection at a fraction of current prevention spending
AU - Romain Puech
AU - Danique de Moor
AU - Ana Trišović
AU - Dimitris Bertsimas
PY - 2026
DA - 2026/09/16
PB - arXiv
DO - 10.48550/arxiv.2609.18556
UR - https://arxiv.org/abs/2609.18556
AB - 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.
ER -