Title: WFDroneBench: A Benchmark for Sensor Placement and Drone Routing for Wildfire Detection
Primary area: datasets and benchmarks
Keywords: Wildfire, Optimization, benchmark, dataset

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, operations research experts can compare routing strategies, policymakers can explore resource trade-offs, and engineers can evaluate how drone specs impact detection. 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, especially Team Orienteering Problem (TOP) and Max-Coverage, significantly outperform other baselines, with TOP achieving the fastest detection on the most difficult fires. We openly release all code, data, and documentation.

TL;DR: Dataset and benchmarking environment for drone routing and sensor placement strategies in wildfire detection