SAREnv: An Open-Source Dataset and Benchmark Tool for Informed Wilderness Search and Rescue Using UAVs

Highlights: What are the main findings? We introduce SAREnv, a novel, open-source benchmark for wilderness search and rescue (SAR), which includes a dataset of 60 high-resolution geospatial scenarios and probabilistic lost person models derived from empirical data. A comprehensive evaluation framework is provided with four baseline multi-agent path-planning algorithms, three quantitative performance metrics, and tools that enable users to generate custom synthetic SAR datasets. What is the implication of the main finding? This work addresses a deficiency in SAR research by providing a standardized framework for the systematic and reproducible comparison of UAV search strategies. The open-source nature of this benchmark is intended to accelerate innovation by enabling researchers to rigorously validate novel algorithms against established baselines, facilitating the development of more effective autonomous systems for real-world operations. Unmanned aerial vehicles (UAVs) play an increasingly vital role in wilderness search and rescue (SAR) operations by enhancing situational awareness and extending the capabilities of human teams. Yet, a lack of standardized benchmarks has impeded the systematic evaluation of single- and multi-agent path-planning algorithms. This paper introduces an open-source dataset and evaluation framework to address this gap. The framework comprises 60 geospatial scenarios across four distinct European environments, featuring high-resolution probability maps. We present a lost person probabilistic model derived from statistical models of lost person behavior. We provide a suite of tools for evaluating search paths against four baseline methods: Concentric Circles, Pizza Zigzag, Greedy, and Random Exploration, using three quantitative metrics: Accumulated probability of detection, time-discounted probability of detection, and lost person discovery score. We provide an evaluation framework to facilitate the comparative analysis of single- and multi-agent path-planning algorithms, supporting both the baseline methods presented and custom user-defined path generators. By providing a structured and extensible framework, this work establishes a foundation for the rigorous and reproducible assessment of UAV search strategies in complex wilderness environments.