WFDroneBench: A Benchmark for Sensor Placement and Drone Routing for Wildfire Detection

Romain Puech, Joseph Ye, Danique de Moor, Ana Trisovic, Dimitris Bertsimas

Keywords: Wildfire, Optimization, benchmark, dataset

TL;DR: Dataset and benchmarking environment for drone routing and sensor placement strategies in 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 in- troduce 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 operations research experts can compare routing strate- gies. 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 four drone routing strategies. Our experiments show that risk-aware strategies – Team Orienteering Problem (TOP) and Max-Coverage – significantly outperform other baselines when risk maps are sufficiently accurate, with TOP achieving the fastest detection on the most difficult fires. We further find that risk-aware static infrastructure helps even under an imperfect riskmap and drone-based detection outperforms ground sensors. Finally, our results reveal two key open challenges: (i) detecting small fires rapidly and reliably, and (ii) improv- ing risk-map prediction, where the gap between ground-truth ignition patterns and available risk maps highlights a significant opportunity for ML innovation.

Primary Area: datasets and benchmarks

Submission Number: 22827